From 3d699a15ac377aa1104433a3c764ab5df9854e89 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Sun, 20 Dec 2020 13:28:55 +0000 Subject: [PATCH 01/20] GitBook: [master] 12 pages modified --- README.md | 42 ++++++- SUMMARY.md | 14 +++ artificial-intelligence.md | 41 +++++++ cheat-sheets.md | 6 + deep-learning-1.md | 194 +++++++++++++++++++++++++++++++++ extra.md | 27 +++++ genetic-algorithms.md | 10 ++ interview-resources.md | 9 ++ machine-learning.md | 18 +++ natural-language-processing.md | 63 +++++++++++ statistics.md | 16 +++ useful-blogs.md | 23 ++++ 12 files changed, 462 insertions(+), 1 deletion(-) create mode 100644 SUMMARY.md create mode 100644 artificial-intelligence.md create mode 100644 cheat-sheets.md create mode 100644 deep-learning-1.md create mode 100644 extra.md create mode 100644 genetic-algorithms.md create mode 100644 interview-resources.md create mode 100644 machine-learning.md create mode 100644 natural-language-processing.md create mode 100644 statistics.md create mode 100644 useful-blogs.md diff --git a/README.md b/README.md index b60b94e..cd4ffb4 100644 --- a/README.md +++ b/README.md @@ -1 +1,41 @@ -# MLI-Resource-Book \ No newline at end of file +--- +description: >- + As you know we are committed to transparency in everything we do. It's why we + always share resources and information on AI/ML. It's also why we compiled a + list of study materials for you!. +--- + +# Introduction + +## About Us + +Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving community of over **350,000** ardent - artificial intelligence and machine learning enthusiasts across India and the globe. We at MLI, believe that India has the potential to position itself among leaders, on the global technology map. + +The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI ecosystem and talent pool; thereby leading our country to have a significant take in the global AI revolution_. To pursue the same, we intend to democratize quality **technical education**, **resources,** and **opportunities** and make them available to all. + +## Table of Contents + +{% page-ref page="./" %} + +{% page-ref page="artificial-intelligence.md" %} + +{% page-ref page="machine-learning.md" %} + +{% page-ref page="deep-learning-1.md" %} + +{% page-ref page="interview-resources.md" %} + +{% page-ref page="genetic-algorithms.md" %} + +{% page-ref page="statistics.md" %} + +{% page-ref page="useful-blogs.md" %} + +{% page-ref page="cheat-sheets.md" %} + +{% page-ref page="natural-language-processing.md" %} + +{% page-ref page="extra.md" %} + + + diff --git a/SUMMARY.md b/SUMMARY.md new file mode 100644 index 0000000..fd4164f --- /dev/null +++ b/SUMMARY.md @@ -0,0 +1,14 @@ +# Table of contents + +* [Introduction](README.md) +* [Artificial Intelligence](artificial-intelligence.md) +* [Machine Learning](machine-learning.md) +* [Deep Learning](deep-learning-1.md) +* [Interview Resources](interview-resources.md) +* [Genetic Algorithms](genetic-algorithms.md) +* [Statistics](statistics.md) +* [Useful Blogs](useful-blogs.md) +* [Cheat Sheets](cheat-sheets.md) +* [Natural Language Processing](natural-language-processing.md) +* [Extra](extra.md) + diff --git a/artificial-intelligence.md b/artificial-intelligence.md new file mode 100644 index 0000000..fa4402f --- /dev/null +++ b/artificial-intelligence.md @@ -0,0 +1,41 @@ +# Artificial Intelligence + +* [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) +* [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) +* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) +* [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) +* [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) +* [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) +* [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) +* ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ +* ​[Artificial Intelligence](https://career.guru99.com/recommends/artificialintelligence-2/)​ +* ​[Artificial Intelligence Nanodegree](https://in.udacity.com/course/artificial-intelligence-nanodegree--nd889)​ +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#1_Artificial_Intelligence_Northwestern_Kellog_School_of_Management)​ +* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#2_Machine_Learning_AI_Certification_by_Stanford_University_Coursera)​ +* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#3_IBM_AI_Engineering_Professional_Certificate_Coursera)​ +* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#4_Artificial_Intelligence_Business_Strategies_and_Applications_Berkeley_ExecEd)​ +* ​[Deep Learning by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#5_Deep_Learning_by_Andrew_Ng_Coursera)​ +* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#6_AI_For_Everyone_by_Andrew_Ng_Coursera)​ +* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#7_Artificial_Intelligence_Certification_by_Columbia_University_edX)​ +* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#8_Introduction_to_Artificial_Intelligence_by_IBM_Coursera)​ +* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#9_Microsoft_Professional_Certification_in_Artificial_Intelligence_edX)​ +* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#10_TensorFlow_for_Artificial_Intelligence_by_deeplearningai_Coursera)​ +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management\)](http://emeritus-institute-of-management.sjv.io/c/397676/674046/8201)​ +* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ +* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainewcert1&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fai-engineer)​ +* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](http://emeritus-institute-of-management.sjv.io/c/397676/754488/8201)​ +* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai3&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fai-for-everyone)​ +* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai5&p=https%3A%2F%2Fwww.edx.org%2Fmicromasters%2Fcolumbiax-artificial-intelligence)​ +* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainew1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-to-ai)​ +* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai6&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-artificial-intelligence)​ +* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai7&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-tensorflow)​ +* [Free Artificial Intelligence Courses \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddainew2&p=%5B%5Bhttps%253A%252F%252Fwww.edx.org%252Flearn%252Fartificial-intelligence%5D%5D)​ +* ​[Artificial Intelligence Courses \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddainew4&murl=https%3A%2F%2Fwww.udemy.com%2Ftopic%2Fartificial-intelligence%2F)​ +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ +* ​[Advanced AI Tutorial: Deep Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddai11&murl=https%3A%2F%2Fwww.udemy.com%2Fdeep-reinforcement-learning-in-python%2F)​ +* [Artificial Intelligence Course: Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-reinforcement-learning-in-python%2F)​ +* ​[Python for Everybody by University of Michigan \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai15&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpython)​ +* ​[Artificial Intelligence for Beginners – Free Course \(LinkedIn Learning – Lynda\)](https://linkedin-learning.pxf.io/c/1238999/449670/8005?subId1=ddartificial1&u=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fartificial-intelligence-foundations-thinking-machines%2F)​ +* ​[Artificial Intelligence Certification: Learn How To Build An AI \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-az%2F)​ +* ​[Artificial Intelligence Course with Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fdata-science-and-machine-learning-with-python-hands-on%2F)​ + diff --git a/cheat-sheets.md b/cheat-sheets.md new file mode 100644 index 0000000..decd51c --- /dev/null +++ b/cheat-sheets.md @@ -0,0 +1,6 @@ +# Cheat Sheets + +* [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) +* [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) +* [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) + diff --git a/deep-learning-1.md b/deep-learning-1.md new file mode 100644 index 0000000..6aac458 --- /dev/null +++ b/deep-learning-1.md @@ -0,0 +1,194 @@ +# Deep Learning + +* [Complete Guide to TensorFlow for Deep Learning with Python](https://career.guru99.com/recommends/deeplearning-2/) +* [Deep Learning Specialization](https://career.guru99.com/recommends/deeplearning-1/) +* [Deep Learning with Python and Keras](https://career.guru99.com/recommends/deeplearning-3/) +* [Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs](https://career.guru99.com/recommends/deeplearning-4/) +* [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://career.guru99.com/recommends/deeplearning-5/) +* [Natural Language Processing with Deep Learning in Python](https://career.guru99.com/recommends/deeplearning-6/) +* [Deep Learning by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai2&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) +* [IBM Data Science Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai12&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fibm-data-science-professional-certificate) +* [Deep Learning by IBM \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai17&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fibm-deep-learning) +* [Data Science by Harvard University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai18&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fharvardx-data-science) +* [Microsoft Professional Program in Data Science \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai19&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-data-science) +* [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) +* [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html) +* [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) +* [**Deep Learning Papers Reading Roadmap**](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md) +* [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) +* [Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) +* [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +* [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) +* [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) +* [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) +* [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) +* [Recent Reddit AMAs related to Deep Learning](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [Another AMA](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/) +* [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) +* [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +* [Introduction to Deep Learning Using Python \(GitHub\)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) +* [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) +* [Deep Learning Software List](http://deeplearning.net/software_links/) +* [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) +* [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) +* [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) +* [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) +* [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) +* [deeplearning Tutorials](http://deeplearning4j.org/) +* [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) +* [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) +* [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) +* [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) +* [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) +* [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) +* [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) +* [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) +* [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) +* Neural Machine Translation + * [**Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list) + * [Introduction to Neural Machine Translation with GPUs \(part 1\)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) + * [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) + + + +* Deep Learning Frameworks + * * [Torch vs. Theano](http://fastml.com/torch-vs-theano/) + * [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) + * [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) + * [Theano](https://en.wikipedia.org/wiki/Theano_%28software%29) + * [Website](http://deeplearning.net/software/theano/) + * [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) + * [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/) + * [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/) + * [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg) + * [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp) + * [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet) + * [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu) + * [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) + * [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) + * [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) + * [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) + * [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) + * [Torch](http://torch.ch/) + * [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) + * [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) + * [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) + * [Awesome-Torch \(Repository on GitHub\)](https://github.com/carpedm20/awesome-torch) + * [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) + * [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) + * [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet) + * [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) + * Caffe + * [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) + * TensorFlow + * [Website](http://tensorflow.org/) + * [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) + * [Stanford Tensorflow for Deep Learning Research Course](https://web.stanford.edu/class/cs20si/syllabus.html) + * [GitHub Repo](https://github.com/chiphuyen/tf-stanford-tutorials) + * [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) + * [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) + * [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) + * [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) + * [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) + * [Android TensorFlow Machine Learning Example](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) + * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) + * [Creating Custom Model For Android Using TensorFlow](https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb) + * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample) + + + +* Feed Forward Networks + * * [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) + * [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) + * [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) + * [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) + * [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) + * [Choosing number of hidden layers and nodes](http://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#) + * [Backpropagation in Matrix Form](http://sudeepraja.github.io/Neural/) + * [ANN implemented in C++ \| AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) + * [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) + * [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) + * [Regression and Classification with NNs \(Slides\)](http://www.autonlab.org/tutorials/neural13.pdf) + * [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) + + + +* Recurrent and LSTM Networks + * * [awesome-rnn: list of resources \(GitHub Repo\)](https://github.com/kjw0612/awesome-rnn) + * [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) + * [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) + * [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) + * [Intro to RNN](http://deeplearning4j.org/recurrentnetwork.html), [LSTM](http://deeplearning4j.org/lstm.html) + * [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) + * [Optimizing RNN Performance](http://svail.github.io/) + * [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) + * [Auto-Generating Clickbait with RNN](https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) + * [Sequence Learning using RNN \(Slides\)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) + * [Machine Translation using RNN \(Paper\)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) + * [Music generation using RNNs \(Keras\)](https://github.com/MattVitelli/GRUV) + * [Using RNN to create on-the-fly dialogue \(Keras\)](http://neuralniche.com/post/tutorial/) + * Long Short Term Memory \(LSTM\) + * [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) + * [LSTM explained](https://apaszke.github.io/lstm-explained.html) + * [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html) + * [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/), [Python/Theano code](https://github.com/dennybritz/rnn-tutorial-gru-lstm) + * [Torch Code for character-level language models using LSTM](https://github.com/karpathy/char-rnn) + * [LSTM for Kaggle EEG Detection competition \(Torch Code\)](https://github.com/apaszke/kaggle-grasp-and-lift) + * [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) + * [Deep Learning for Visual Q&A \| LSTM \| CNN](http://avisingh599.github.io/deeplearning/visual-qa/), [Code](https://github.com/avisingh599/visual-qa) + * [Computer Responds to email using LSTM \| Google](http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html) + * [LSTM dramatically improves Google Voice Search](http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html), [Another Article](http://deeplearning.net/2015/09/30/long-short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/) + * [Understanding Natural Language with LSTM Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) + * [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) + * [LSTM for Human Activity Recognition](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/) + * Gated Recurrent Units \(GRU\) + * [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) + * [Time series forecasting with Sequence-to-Sequence \(seq2seq\) rnn models](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) + + + +* [Recursive Neural Network \(not Recurrent\)](https://en.wikipedia.org/wiki/Recursive_neural_network) + * [Recursive Neural Tensor Network \(RNTN\)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) + * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + + + +* Restricted Boltzmann Machine + * * [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) + * [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) + * [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) + * [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) + * [RBMs in R](https://github.com/zachmayer/rbm) + * [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) + * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + + + +* Autoencoders: Unsupervised \(applies BackProp after setting target = input\) + * * [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) + * [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) + * [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) + * [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) + + + +* Convolutional Neural Networks + * * [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) + * [Awesome Deep Vision: List of Resources \(GitHub\)](https://github.com/kjw0612/awesome-deep-vision) + * [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) + * [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) + * [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) + * [JavaScript Library \(Browser Based\) for CNNs](http://cs.stanford.edu/people/karpathy/convnetjs/) + * [Using CNNs to detect facial keypoints](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) + * [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) + * [Interview with Yann LeCun \| Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) + * [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) + + + +* Network Representation Learning + * * [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) + * [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) + * [Network Representation Learning Papers](https://github.com/thunlp) + * [Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) + * [Graph Based Deep Learning Literature](https://github.com/naganandy/graph-based-deep-learning-literature) + diff --git a/extra.md b/extra.md new file mode 100644 index 0000000..b9769df --- /dev/null +++ b/extra.md @@ -0,0 +1,27 @@ +# Extra + +* [Machine Learning Course by Andrew Ng \(Stanford University\)](https://www.coursera.org/learn/machine-learning) +* [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) +* [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) +* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) +* [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) +* [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) +* [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) +* [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) +* [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) +* [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) +* [The Open Source Data Science Masters](http://datasciencemasters.org/) +* [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) +* [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) +* [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) +* [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) +* [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) +* [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) +* [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) +* [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) +* [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) +* [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) +* [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) +* [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) +* [Twitter's Most Shared \#machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) + diff --git a/genetic-algorithms.md b/genetic-algorithms.md new file mode 100644 index 0000000..abdd509 --- /dev/null +++ b/genetic-algorithms.md @@ -0,0 +1,10 @@ +# Genetic Algorithms + +* [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) +* [Simple Implementation of Genetic Algorithms in Python \(Part 1\)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) +* [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) +* [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) +* [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) + * [Genetic Programming in Python \(GitHub\)](https://github.com/trevorstephens/gplearn) + * [Genetic Alogorithms vs Genetic Programming \(Quora\)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) + diff --git a/interview-resources.md b/interview-resources.md new file mode 100644 index 0000000..f36d51b --- /dev/null +++ b/interview-resources.md @@ -0,0 +1,9 @@ +# Interview Resources + +* [41 Essential Machine Learning Interview Questions \(with answers\)](https://www.springboard.com/blog/machine-learning-interview-questions/) +* [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) +* [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) +* [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) +* [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) +* [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63) + diff --git a/machine-learning.md b/machine-learning.md new file mode 100644 index 0000000..0e30c54 --- /dev/null +++ b/machine-learning.md @@ -0,0 +1,18 @@ +# Machine Learning + +* ​[Machine Learning A-Z](https://career.guru99.com/recommends/machinelearning-1/)​ +* ​[Machine Learning Specialization](https://career.guru99.com/recommends/machinelearning-2/)​ +* ​[Principles of Machine Learning](https://career.guru99.com/recommends/machinelearning-3/)​ +* ​[Advanced Machine Learning](https://career.guru99.com/recommends/machinelearning-4/)​ +* ​[Learn Machine Learning](https://www.anrdoezrs.net/links/5424943/type/dlg/https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t)​ +* ​[Python for Data Science and Machine Learning Bootcamp](https://career.guru99.com/recommends/machinelearning-5/)​ +* ​[Scala and Spark for Big Data and Machine Learning](https://career.guru99.com/recommends/machinelearning-6/)​ +* ​[Machine Learning, Data Science and Deep Learning with Python](https://career.guru99.com/recommends/machinelearning-7/)​ +* ​[Data Science and Machine Learning Bootcamp with R](https://career.guru99.com/recommends/machinelearning-8/)​ +* ​[Machine Learning \(By Georgia Tech\)](https://career.guru99.com/recommends/machinelearning-9/)​ +* ​[Machine Learning \(By Columbia University\)](https://career.guru99.com/recommends/machinelearning-10/)​ +* ​[Robotics: Vision Intelligence and Machine Learning](https://career.guru99.com/recommends/machinelearning-11/)​ +* **​**[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ +* ​[Machine Learning Certification from University of Washington \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning)​ + diff --git a/natural-language-processing.md b/natural-language-processing.md new file mode 100644 index 0000000..73d5ec1 --- /dev/null +++ b/natural-language-processing.md @@ -0,0 +1,63 @@ +# Natural Language Processing + + + +* [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) +* [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) +* [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) +* [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) +* [The Stanford NLP Group](https://nlp.stanford.edu/) +* [NLP from Scratch \| Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) +* [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) +* [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) + * [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) +* * Topic Modeling + * * [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) + * [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) + * [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) + * [What is a good explanation of Latent Dirichlet Allocation \(LDA\)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) + * [**Introduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) + * [The LDA Buffet - Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) + * [Your Guide to Latent Dirichlet Allocation \(LDA\)](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) + * [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) + * [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) + * [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) + * [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) + * [topicmodels: An R Package for Fitting Topic Models](https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf) + * [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) + * [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) + * [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) + * [Segmentation of Twitter Timelines via Topic Modeling](https://alexisperrier.com/nlp/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) + * [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) + * [Multilingual Latent Dirichlet Allocation \(LDA\)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). \([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)\) + * [Deep Belief Nets for Topic Modeling](https://github.com/larsmaaloee/deep-belief-nets-for-topic-modeling) + * [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) + * Python + * [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) + * [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) +* * word2vec + * * [Google word2vec](https://code.google.com/archive/p/word2vec) + * [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) + * [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) + * [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) + * [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) + * [Word Vectors Kaggle Tutorial Python](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) + * [Making sense of word2vec](http://rare-technologies.com/making-sense-of-word2vec/) + * [word2vec explained on deeplearning4j](http://deeplearning4j.org/word2vec.html) + * [Quora word2vec](https://www.quora.com/How-does-word2vec-work) + * [Other Quora Resources](https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms), [2](https://www.quora.com/What-is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why) + * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) +* Text Clustering + * [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) + * [Levenshtein distance for measuring the difference between two sequences](https://en.wikipedia.org/wiki/Levenshtein_distance) + * [Text clustering with Levenshtein distances](http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances) +* Text Classification + * [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) +* Named Entity Recognitation + * [Stanford Named Entity Recognizer \(NER\)](https://nlp.stanford.edu/software/CRF-NER.shtml) + * [Named Entity Recognition: Applications and Use Cases- Towards Data Science](https://towardsdatascience.com/named-entity-recognition-applications-and-use-cases-acdbf57d595e) +* [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) +* [Kaggle Tutorial Bag of Words and Word vectors](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) +* [What would Shakespeare say \(NLP Tutorial\)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) +* [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) + diff --git a/statistics.md b/statistics.md new file mode 100644 index 0000000..a0d1ccc --- /dev/null +++ b/statistics.md @@ -0,0 +1,16 @@ +# Statistics + +* [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics +* [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach +* [Statistics for Hackers \| Slides \| @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas +* [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics +* [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) +* Tutorials + * [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) + * [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) + * [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) +* [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) +* [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) +* [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) +* [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook + diff --git a/useful-blogs.md b/useful-blogs.md new file mode 100644 index 0000000..e691312 --- /dev/null +++ b/useful-blogs.md @@ -0,0 +1,23 @@ +# Useful Blogs + +* [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science +* [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! +* [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning +* [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general +* [Colah's Blog](http://colah.github.io/) - Awesome Neural Networks Blog +* [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering +* [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog +* [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors +* [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond +* [fastML](http://fastml.com/) - Machine learning made easy +* [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page +* [no free hunch \| kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science +* [A Quantitative Journey \| outlace](http://outlace.com/) - learning quantitative applications +* [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R +* [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog +* [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence +* [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) - Making deep learning accessible +* [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets +* [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning +* [Ethen's Notebook Collection](https://github.com/ethen8181/machine-learning) - Continuously updated machine learning documentations \(mainly in Python3\). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage + From eba35924262b5196d130fec99bdb4e859f4d0878 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Tue, 12 Jan 2021 08:04:01 +0000 Subject: [PATCH 02/20] GitBook: [master] one page modified --- artificial-intelligence.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index fa4402f..55445ee 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -1,8 +1,8 @@ # Artificial Intelligence -* [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) -* [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) -* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) +* [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) A curated list of Artificial Intelligence \(AI\) courses, books, video lectures and papers. +* [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) Materials developed for UC Berkeley's introductory artificial intelligence course +* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) * [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) * [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) * [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) From b7a68eaa6508ba48833c210f65eb9b79267fc16a Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Tue, 12 Jan 2021 08:04:29 +0000 Subject: [PATCH 03/20] GitBook: [master] one page modified --- artificial-intelligence.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index 55445ee..bf606a9 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -2,7 +2,7 @@ * [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) A curated list of Artificial Intelligence \(AI\) courses, books, video lectures and papers. * [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) Materials developed for UC Berkeley's introductory artificial intelligence course -* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) +* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) * [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) * [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) * [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) From e1384835ee730cf77c030c1ed04737eac9db67c3 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Mon, 18 Jan 2021 06:09:09 +0000 Subject: [PATCH 04/20] GitBook: [master] one page modified --- artificial-intelligence.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index bf606a9..62d9b19 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -2,7 +2,7 @@ * [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) A curated list of Artificial Intelligence \(AI\) courses, books, video lectures and papers. * [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) Materials developed for UC Berkeley's introductory artificial intelligence course -* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) +* [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) Hackr.io is a community to find and share the best online courses & tutorials. Join them, it only takes 30 seconds. * [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) * [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) * [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) From 28d405390835ec03bda66c75d31a2540db89f333 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Mon, 18 Jan 2021 06:11:56 +0000 Subject: [PATCH 05/20] GitBook: [master] one page modified --- artificial-intelligence.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index 62d9b19..c61f428 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -3,8 +3,8 @@ * [Awesome Artificial Intelligence \(GitHub Repo\)](https://github.com/owainlewis/awesome-artificial-intelligence) A curated list of Artificial Intelligence \(AI\) courses, books, video lectures and papers. * [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM) Materials developed for UC Berkeley's introductory artificial intelligence course * [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) Hackr.io is a community to find and share the best online courses & tutorials. Join them, it only takes 30 seconds. -* [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) -* [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) +* [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. +* [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) This course focuses on Intro to AI and other basic concepts in AI. * [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) * [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) * ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ From d611805af4e9e9d0f1f7b216b4fc3c05fee64d9f Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 08:18:02 +0000 Subject: [PATCH 06/20] GitBook: [master] one page modified --- artificial-intelligence.md | 59 +++++++++++++++++--------------------- 1 file changed, 27 insertions(+), 32 deletions(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index c61f428..6e0038f 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -5,37 +5,32 @@ * [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) Hackr.io is a community to find and share the best online courses & tutorials. Join them, it only takes 30 seconds. * [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. * [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) This course focuses on Intro to AI and other basic concepts in AI. -* [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) -* [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) -* ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ -* ​[Artificial Intelligence](https://career.guru99.com/recommends/artificialintelligence-2/)​ +* [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) This course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. +* [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) Computers are being taught to learn, reason and recognize emotions. In these talks, look for insights — as well as warnings. +* ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! +* ​[Artificial Intelligence](https://career.guru99.com/recommends/artificialintelligence-2/)​ Learn the fundamentals of Artificial Intelligence \(AI\), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. * ​[Artificial Intelligence Nanodegree](https://in.udacity.com/course/artificial-intelligence-nanodegree--nd889)​ -* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#1_Artificial_Intelligence_Northwestern_Kellog_School_of_Management)​ -* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#2_Machine_Learning_AI_Certification_by_Stanford_University_Coursera)​ -* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#3_IBM_AI_Engineering_Professional_Certificate_Coursera)​ -* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#4_Artificial_Intelligence_Business_Strategies_and_Applications_Berkeley_ExecEd)​ -* ​[Deep Learning by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#5_Deep_Learning_by_Andrew_Ng_Coursera)​ -* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#6_AI_For_Everyone_by_Andrew_Ng_Coursera)​ -* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#7_Artificial_Intelligence_Certification_by_Columbia_University_edX)​ -* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#8_Introduction_to_Artificial_Intelligence_by_IBM_Coursera)​ -* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#9_Microsoft_Professional_Certification_in_Artificial_Intelligence_edX)​ -* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#10_TensorFlow_for_Artificial_Intelligence_by_deeplearningai_Coursera)​ -* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management\)](http://emeritus-institute-of-management.sjv.io/c/397676/674046/8201)​ -* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ -* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainewcert1&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fai-engineer)​ -* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](http://emeritus-institute-of-management.sjv.io/c/397676/754488/8201)​ -* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai3&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fai-for-everyone)​ -* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai5&p=https%3A%2F%2Fwww.edx.org%2Fmicromasters%2Fcolumbiax-artificial-intelligence)​ -* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainew1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-to-ai)​ -* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai6&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-artificial-intelligence)​ -* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai7&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-tensorflow)​ -* [Free Artificial Intelligence Courses \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddainew2&p=%5B%5Bhttps%253A%252F%252Fwww.edx.org%252Flearn%252Fartificial-intelligence%5D%5D)​ -* ​[Artificial Intelligence Courses \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddainew4&murl=https%3A%2F%2Fwww.udemy.com%2Ftopic%2Fartificial-intelligence%2F)​ -* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ -* ​[Advanced AI Tutorial: Deep Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddai11&murl=https%3A%2F%2Fwww.udemy.com%2Fdeep-reinforcement-learning-in-python%2F)​ -* [Artificial Intelligence Course: Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-reinforcement-learning-in-python%2F)​ -* ​[Python for Everybody by University of Michigan \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai15&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpython)​ -* ​[Artificial Intelligence for Beginners – Free Course \(LinkedIn Learning – Lynda\)](https://linkedin-learning.pxf.io/c/1238999/449670/8005?subId1=ddartificial1&u=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fartificial-intelligence-foundations-thinking-machines%2F)​ -* ​[Artificial Intelligence Certification: Learn How To Build An AI \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-az%2F)​ -* ​[Artificial Intelligence Course with Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fdata-science-and-machine-learning-with-python-hands-on%2F)​ + + Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more. + +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#1_Artificial_Intelligence_Northwestern_Kellog_School_of_Management)​ This course is designed for senior leaders, managers, and functional business heads who are interested in exploring AI opportunities across their business functions. +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management\)](http://emeritus-institute-of-management.sjv.io/c/397676/674046/8201)​ Artificial Intelligence \(AI\) has moved into the mainstream, driven by advances in cloud computing, big data, open source software, and improved algorithms. AI technologies are fundamentally altering how we work, live, and manage businesses. +* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. +* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainewcert1&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fai-engineer)​ Artificial intelligence \(AI\) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. +* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](http://emeritus-institute-of-management.sjv.io/c/397676/754488/8201)​ This program helps introduce basic applications of AI to those in business. While participants learn about AI’s current capabilities and potential, they also gain more depth with attention to the reach of automation, machine learning, and robotics. +* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai3&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fai-for-everyone)​ AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. +* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai5&p=https%3A%2F%2Fwww.edx.org%2Fmicromasters%2Fcolumbiax-artificial-intelligence)​ Gain expertise in one of the most fascinating and fastest growing areas of computer science through an innovative online program that covers fascinating and compelling topics in the field of Artificial Intelligence and its applications. +* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainew1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-to-ai)​ In this course you will learn what Artificial Intelligence \(AI\) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. +* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai6&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-artificial-intelligence)​ Microsoft courses found here can be audited free or students can choose to receive a verified certificate for a small fee. +* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai7&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-tensorflow)​ This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. +* [Free Artificial Intelligence Courses \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddainew2&p=%5B%5Bhttps%253A%252F%252Fwww.edx.org%252Flearn%252Fartificial-intelligence%5D%5D)​ Learn artificial intelligence by studying natural language processing, reinforcement learning, predictive analytics, deep neural networks, image processing, the human brain, and more today!. +* ​[Artificial Intelligence Courses \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddainew4&murl=https%3A%2F%2Fwww.udemy.com%2Ftopic%2Fartificial-intelligence%2F)​ Udemy offers top-rated artificial intelligence courses that will walk you through combining deep learning, machine learning, and data science practices to build your own AI and solve unique problems in any industry. +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects. +* ​[Advanced AI Tutorial: Deep Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddai11&murl=https%3A%2F%2Fwww.udemy.com%2Fdeep-reinforcement-learning-in-python%2F)​ The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks +* [Artificial Intelligence Course: Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-reinforcement-learning-in-python%2F)​ Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications +* ​[Python for Everybody by University of Michigan \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai15&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpython)​ Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data. +* ​[Artificial Intelligence for Beginners – Free Course \(LinkedIn Learning – Lynda\)](https://linkedin-learning.pxf.io/c/1238999/449670/8005?subId1=ddartificial1&u=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fartificial-intelligence-foundations-thinking-machines%2F)​ This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. +* ​[Artificial Intelligence Certification: Learn How To Build An AI \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-az%2F)​ Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! +* ​[Artificial Intelligence Course with Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fdata-science-and-machine-learning-with-python-hands-on%2F)​ + Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks From 05aa5498082494cafade2e930a4c6a20c76bb8d0 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 09:22:27 +0000 Subject: [PATCH 07/20] GitBook: [master] 2 pages modified --- artificial-intelligence.md | 59 +++++++++++++++++++++----------------- machine-learning.md | 31 +++++++++++--------- 2 files changed, 50 insertions(+), 40 deletions(-) diff --git a/artificial-intelligence.md b/artificial-intelligence.md index 6e0038f..c61f428 100644 --- a/artificial-intelligence.md +++ b/artificial-intelligence.md @@ -5,32 +5,37 @@ * [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) Hackr.io is a community to find and share the best online courses & tutorials. Join them, it only takes 30 seconds. * [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/) In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. * [edX course \| Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) This course focuses on Intro to AI and other basic concepts in AI. -* [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) This course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. -* [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) Computers are being taught to learn, reason and recognize emotions. In these talks, look for insights — as well as warnings. -* ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! -* ​[Artificial Intelligence](https://career.guru99.com/recommends/artificialintelligence-2/)​ Learn the fundamentals of Artificial Intelligence \(AI\), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. +* [Udacity Course \| Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) +* [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) +* ​[Artificial Intelligence A-Z™: Learn How To Build An AI](https://career.guru99.com/recommends/artificialintelligence-1/)​ +* ​[Artificial Intelligence](https://career.guru99.com/recommends/artificialintelligence-2/)​ * ​[Artificial Intelligence Nanodegree](https://in.udacity.com/course/artificial-intelligence-nanodegree--nd889)​ - - Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more. - -* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#1_Artificial_Intelligence_Northwestern_Kellog_School_of_Management)​ This course is designed for senior leaders, managers, and functional business heads who are interested in exploring AI opportunities across their business functions. -* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management\)](http://emeritus-institute-of-management.sjv.io/c/397676/674046/8201)​ Artificial Intelligence \(AI\) has moved into the mainstream, driven by advances in cloud computing, big data, open source software, and improved algorithms. AI technologies are fundamentally altering how we work, live, and manage businesses. -* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. -* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainewcert1&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fai-engineer)​ Artificial intelligence \(AI\) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. -* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](http://emeritus-institute-of-management.sjv.io/c/397676/754488/8201)​ This program helps introduce basic applications of AI to those in business. While participants learn about AI’s current capabilities and potential, they also gain more depth with attention to the reach of automation, machine learning, and robotics. -* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai3&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fai-for-everyone)​ AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. -* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai5&p=https%3A%2F%2Fwww.edx.org%2Fmicromasters%2Fcolumbiax-artificial-intelligence)​ Gain expertise in one of the most fascinating and fastest growing areas of computer science through an innovative online program that covers fascinating and compelling topics in the field of Artificial Intelligence and its applications. -* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainew1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-to-ai)​ In this course you will learn what Artificial Intelligence \(AI\) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. -* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai6&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-artificial-intelligence)​ Microsoft courses found here can be audited free or students can choose to receive a verified certificate for a small fee. -* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai7&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-tensorflow)​ This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. -* [Free Artificial Intelligence Courses \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddainew2&p=%5B%5Bhttps%253A%252F%252Fwww.edx.org%252Flearn%252Fartificial-intelligence%5D%5D)​ Learn artificial intelligence by studying natural language processing, reinforcement learning, predictive analytics, deep neural networks, image processing, the human brain, and more today!. -* ​[Artificial Intelligence Courses \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddainew4&murl=https%3A%2F%2Fwww.udemy.com%2Ftopic%2Fartificial-intelligence%2F)​ Udemy offers top-rated artificial intelligence courses that will walk you through combining deep learning, machine learning, and data science practices to build your own AI and solve unique problems in any industry. -* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects. -* ​[Advanced AI Tutorial: Deep Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddai11&murl=https%3A%2F%2Fwww.udemy.com%2Fdeep-reinforcement-learning-in-python%2F)​ The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks -* [Artificial Intelligence Course: Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-reinforcement-learning-in-python%2F)​ Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications -* ​[Python for Everybody by University of Michigan \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai15&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpython)​ Learn to Program and Analyze Data with Python. Develop programs to gather, clean, analyze, and visualize data. -* ​[Artificial Intelligence for Beginners – Free Course \(LinkedIn Learning – Lynda\)](https://linkedin-learning.pxf.io/c/1238999/449670/8005?subId1=ddartificial1&u=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fartificial-intelligence-foundations-thinking-machines%2F)​ This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. -* ​[Artificial Intelligence Certification: Learn How To Build An AI \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-az%2F)​ Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! -* ​[Artificial Intelligence Course with Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fdata-science-and-machine-learning-with-python-hands-on%2F)​ - Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#1_Artificial_Intelligence_Northwestern_Kellog_School_of_Management)​ +* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#2_Machine_Learning_AI_Certification_by_Stanford_University_Coursera)​ +* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#3_IBM_AI_Engineering_Professional_Certificate_Coursera)​ +* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#4_Artificial_Intelligence_Business_Strategies_and_Applications_Berkeley_ExecEd)​ +* ​[Deep Learning by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#5_Deep_Learning_by_Andrew_Ng_Coursera)​ +* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#6_AI_For_Everyone_by_Andrew_Ng_Coursera)​ +* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#7_Artificial_Intelligence_Certification_by_Columbia_University_edX)​ +* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#8_Introduction_to_Artificial_Intelligence_by_IBM_Coursera)​ +* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#9_Microsoft_Professional_Certification_in_Artificial_Intelligence_edX)​ +* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://digitaldefynd.com/best-artificial-intelligence-courses-training-certifications/#10_TensorFlow_for_Artificial_Intelligence_by_deeplearningai_Coursera)​ +* ​[Artificial Intelligence \(Northwestern \| Kellog School of Management\)](http://emeritus-institute-of-management.sjv.io/c/397676/674046/8201)​ +* ​[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ +* ​[IBM AI Engineering Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainewcert1&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fai-engineer)​ +* ​[Artificial Intelligence: Business Strategies and Applications \(Berkeley ExecEd\)](http://emeritus-institute-of-management.sjv.io/c/397676/754488/8201)​ +* ​[AI For Everyone by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai3&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fai-for-everyone)​ +* ​[Artificial Intelligence Certification by Columbia University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai5&p=https%3A%2F%2Fwww.edx.org%2Fmicromasters%2Fcolumbiax-artificial-intelligence)​ +* ​[Introduction to Artificial Intelligence by IBM \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddainew1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-to-ai)​ +* ​[Microsoft Professional Certification in Artificial Intelligence \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai6&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-artificial-intelligence)​ +* ​[TensorFlow for Artificial Intelligence by deeplearning.ai \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai7&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fintroduction-tensorflow)​ +* [Free Artificial Intelligence Courses \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddainew2&p=%5B%5Bhttps%253A%252F%252Fwww.edx.org%252Flearn%252Fartificial-intelligence%5D%5D)​ +* ​[Artificial Intelligence Courses \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddainew4&murl=https%3A%2F%2Fwww.udemy.com%2Ftopic%2Fartificial-intelligence%2F)​ +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ +* ​[Advanced AI Tutorial: Deep Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&u1=ddai11&murl=https%3A%2F%2Fwww.udemy.com%2Fdeep-reinforcement-learning-in-python%2F)​ +* [Artificial Intelligence Course: Reinforcement Learning in Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-reinforcement-learning-in-python%2F)​ +* ​[Python for Everybody by University of Michigan \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai15&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpython)​ +* ​[Artificial Intelligence for Beginners – Free Course \(LinkedIn Learning – Lynda\)](https://linkedin-learning.pxf.io/c/1238999/449670/8005?subId1=ddartificial1&u=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fartificial-intelligence-foundations-thinking-machines%2F)​ +* ​[Artificial Intelligence Certification: Learn How To Build An AI \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fartificial-intelligence-az%2F)​ +* ​[Artificial Intelligence Course with Python \(Udemy\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=39197&murl=https%3A%2F%2Fwww.udemy.com%2Fdata-science-and-machine-learning-with-python-hands-on%2F)​ diff --git a/machine-learning.md b/machine-learning.md index 0e30c54..83f6bb3 100644 --- a/machine-learning.md +++ b/machine-learning.md @@ -1,18 +1,23 @@ # Machine Learning -* ​[Machine Learning A-Z](https://career.guru99.com/recommends/machinelearning-1/)​ -* ​[Machine Learning Specialization](https://career.guru99.com/recommends/machinelearning-2/)​ -* ​[Principles of Machine Learning](https://career.guru99.com/recommends/machinelearning-3/)​ +* ​[Machine Learning A-Z](https://career.guru99.com/recommends/machinelearning-1/)​ Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. +* ​[Machine Learning Specialization](https://career.guru99.com/recommends/machinelearning-2/)​ Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. * ​[Advanced Machine Learning](https://career.guru99.com/recommends/machinelearning-4/)​ + + Deep Dive Into The Modern AI Techniques. You will teach computer to see, draw, read, talk, play games and solve industry problems. + * ​[Learn Machine Learning](https://www.anrdoezrs.net/links/5424943/type/dlg/https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t)​ -* ​[Python for Data Science and Machine Learning Bootcamp](https://career.guru99.com/recommends/machinelearning-5/)​ -* ​[Scala and Spark for Big Data and Machine Learning](https://career.guru99.com/recommends/machinelearning-6/)​ -* ​[Machine Learning, Data Science and Deep Learning with Python](https://career.guru99.com/recommends/machinelearning-7/)​ -* ​[Data Science and Machine Learning Bootcamp with R](https://career.guru99.com/recommends/machinelearning-8/)​ -* ​[Machine Learning \(By Georgia Tech\)](https://career.guru99.com/recommends/machinelearning-9/)​ -* ​[Machine Learning \(By Columbia University\)](https://career.guru99.com/recommends/machinelearning-10/)​ -* ​[Robotics: Vision Intelligence and Machine Learning](https://career.guru99.com/recommends/machinelearning-11/)​ -* **​**[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ -* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ -* ​[Machine Learning Certification from University of Washington \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning)​ + + Learn advanced machine learning techniques and algorithms -- including how to package and deploy your models to a production environment. + +* ​[Python for Data Science and Machine Learning Bootcamp](https://career.guru99.com/recommends/machinelearning-5/)​ Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more! +* ​[Scala and Spark for Big Data and Machine Learning](https://career.guru99.com/recommends/machinelearning-6/)​ Learn the latest Big Data technology - Spark and Scala, including Spark 2.0 DataFrames! +* ​[Machine Learning, Data Science and Deep Learning with Python](https://career.guru99.com/recommends/machinelearning-7/)​ Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks +* ​[Data Science and Machine Learning Bootcamp with R](https://career.guru99.com/recommends/machinelearning-8/)​ Learn how to use the R programming language for data science and machine learning and data visualization! +* ​[Machine Learning \(By Georgia Tech\)](https://career.guru99.com/recommends/machinelearning-9/)​ Learn about machine learning, the area of artificial intelligence \(AI\) that is concerned with computational artifacts that modify and improve performance through experience. +* ​[Machine Learning \(By Columbia University\)](https://career.guru99.com/recommends/machinelearning-10/)​ Master the essentials of machine learning and algorithms to help improve learning from data without human intervention. +* ​[Robotics: Vision Intelligence and Machine Learning](https://career.guru99.com/recommends/machinelearning-11/)​ Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment. +* **​**[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: \(i\) Supervised learning \(parametric/non-parametric algorithms, support vector machines, kernels, neural networks\). \(ii\) Unsupervised learning \(clustering, dimensionality reduction, recommender systems, deep learning\). \(iii\) Best practices in machine learning \(bias/variance theory; innovation process in machine learning and AI\). +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects. +* ​[Machine Learning Certification from University of Washington \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning)​ Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. From bbb1b855bb5b352682cc8c0b6f04ecd86d41d205 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 14:15:00 +0000 Subject: [PATCH 08/20] GitBook: [master] 2 pages modified --- deep-learning-1.md | 37 +++++++++++++++++++------------------ machine-learning.md | 31 +++++++++++++------------------ 2 files changed, 32 insertions(+), 36 deletions(-) diff --git a/deep-learning-1.md b/deep-learning-1.md index 6aac458..40ba7d5 100644 --- a/deep-learning-1.md +++ b/deep-learning-1.md @@ -1,23 +1,24 @@ # Deep Learning -* [Complete Guide to TensorFlow for Deep Learning with Python](https://career.guru99.com/recommends/deeplearning-2/) -* [Deep Learning Specialization](https://career.guru99.com/recommends/deeplearning-1/) -* [Deep Learning with Python and Keras](https://career.guru99.com/recommends/deeplearning-3/) -* [Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs](https://career.guru99.com/recommends/deeplearning-4/) -* [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://career.guru99.com/recommends/deeplearning-5/) -* [Natural Language Processing with Deep Learning in Python](https://career.guru99.com/recommends/deeplearning-6/) -* [Deep Learning by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai2&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) -* [IBM Data Science Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai12&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fibm-data-science-professional-certificate) -* [Deep Learning by IBM \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai17&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fibm-deep-learning) -* [Data Science by Harvard University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai18&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fharvardx-data-science) -* [Microsoft Professional Program in Data Science \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai19&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-data-science) -* [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) -* [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html) -* [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) -* [**Deep Learning Papers Reading Roadmap**](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md) -* [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) -* [Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) -* [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +* [Complete Guide to TensorFlow for Deep Learning with Python](https://career.guru99.com/recommends/deeplearning-2/) Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! +* [Deep Learning Specialization](https://career.guru99.com/recommends/deeplearning-1/) If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. +* [Deep Learning with Python and Keras](https://career.guru99.com/recommends/deeplearning-3/) Understand and build Deep Learning models for images, text and more using Python and Keras +* [Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs](https://career.guru99.com/recommends/deeplearning-4/) Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. +* [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://career.guru99.com/recommends/deeplearning-5/) Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. +* [Natural Language Processing with Deep Learning in Python](https://career.guru99.com/recommends/deeplearning-6/) Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets. +* [Deep Learning by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai2&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) Become a Deep Learning experts. Master Deep Learning and Break into AI. +* [IBM Data Science Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai12&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fibm-data-science-professional-certificate) Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required. +* [Deep Learning by IBM \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai17&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fibm-deep-learning) This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning. +* [Data Science by Harvard University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai18&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fharvardx-data-science) Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem. +* [Microsoft Professional Program in Data Science \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai19&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-data-science) Microsoft courses found here can be audited free or students can choose to receive a verified certificate for a small fee. +* [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) This course is to make deep learning accessible to as many people as possible. The only prerequisite is that you know how to code \(a year of experience is enough\), preferably in Python, and that you have at least followed a high school math course. +* [fast.ai - Cutting Edge Deep Learning For C](http://course.fast.ai/part2.html)[A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) A curated list of awesome Deep Learning tutorials, projects and communities. +* [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md) The roadmap is constructed in accordance with the following four guidelines: + * From outline to detail + * From old to state-of-the-art + * from generic to specific areas + * focus on state-of-the-art +* [Lots of Deep Learning Resourc](http://deeplearning4j.org/documentation.html)[Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Webs](http://cs224d.stanford.edu/)[Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) * [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) * [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) * [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) diff --git a/machine-learning.md b/machine-learning.md index 83f6bb3..0e30c54 100644 --- a/machine-learning.md +++ b/machine-learning.md @@ -1,23 +1,18 @@ # Machine Learning -* ​[Machine Learning A-Z](https://career.guru99.com/recommends/machinelearning-1/)​ Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. -* ​[Machine Learning Specialization](https://career.guru99.com/recommends/machinelearning-2/)​ Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. +* ​[Machine Learning A-Z](https://career.guru99.com/recommends/machinelearning-1/)​ +* ​[Machine Learning Specialization](https://career.guru99.com/recommends/machinelearning-2/)​ +* ​[Principles of Machine Learning](https://career.guru99.com/recommends/machinelearning-3/)​ * ​[Advanced Machine Learning](https://career.guru99.com/recommends/machinelearning-4/)​ - - Deep Dive Into The Modern AI Techniques. You will teach computer to see, draw, read, talk, play games and solve industry problems. - * ​[Learn Machine Learning](https://www.anrdoezrs.net/links/5424943/type/dlg/https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009t)​ - - Learn advanced machine learning techniques and algorithms -- including how to package and deploy your models to a production environment. - -* ​[Python for Data Science and Machine Learning Bootcamp](https://career.guru99.com/recommends/machinelearning-5/)​ Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more! -* ​[Scala and Spark for Big Data and Machine Learning](https://career.guru99.com/recommends/machinelearning-6/)​ Learn the latest Big Data technology - Spark and Scala, including Spark 2.0 DataFrames! -* ​[Machine Learning, Data Science and Deep Learning with Python](https://career.guru99.com/recommends/machinelearning-7/)​ Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks -* ​[Data Science and Machine Learning Bootcamp with R](https://career.guru99.com/recommends/machinelearning-8/)​ Learn how to use the R programming language for data science and machine learning and data visualization! -* ​[Machine Learning \(By Georgia Tech\)](https://career.guru99.com/recommends/machinelearning-9/)​ Learn about machine learning, the area of artificial intelligence \(AI\) that is concerned with computational artifacts that modify and improve performance through experience. -* ​[Machine Learning \(By Columbia University\)](https://career.guru99.com/recommends/machinelearning-10/)​ Master the essentials of machine learning and algorithms to help improve learning from data without human intervention. -* ​[Robotics: Vision Intelligence and Machine Learning](https://career.guru99.com/recommends/machinelearning-11/)​ Learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment. -* **​**[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: \(i\) Supervised learning \(parametric/non-parametric algorithms, support vector machines, kernels, neural networks\). \(ii\) Unsupervised learning \(clustering, dimensionality reduction, recommender systems, deep learning\). \(iii\) Best practices in machine learning \(bias/variance theory; innovation process in machine learning and AI\). -* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ Whether you’re just learning to code or you’re a seasoned machine learning practitioner, you’ll find information and exercises to help you develop your skills and advance your projects. -* ​[Machine Learning Certification from University of Washington \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning)​ Build Intelligent Applications. Master machine learning fundamentals in four hands-on courses. +* ​[Python for Data Science and Machine Learning Bootcamp](https://career.guru99.com/recommends/machinelearning-5/)​ +* ​[Scala and Spark for Big Data and Machine Learning](https://career.guru99.com/recommends/machinelearning-6/)​ +* ​[Machine Learning, Data Science and Deep Learning with Python](https://career.guru99.com/recommends/machinelearning-7/)​ +* ​[Data Science and Machine Learning Bootcamp with R](https://career.guru99.com/recommends/machinelearning-8/)​ +* ​[Machine Learning \(By Georgia Tech\)](https://career.guru99.com/recommends/machinelearning-9/)​ +* ​[Machine Learning \(By Columbia University\)](https://career.guru99.com/recommends/machinelearning-10/)​ +* ​[Robotics: Vision Intelligence and Machine Learning](https://career.guru99.com/recommends/machinelearning-11/)​ +* **​**[Machine Learning AI Certification by Stanford University \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai1&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning)​ +* ​[Learn AI from ML experts at Google \(Google\)](https://ai.google/education/)​ +* ​[Machine Learning Certification from University of Washington \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning)​ From 8793308b04e82eb66c19f35a4679c249e2aeabff Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 15:11:56 +0000 Subject: [PATCH 09/20] GitBook: [master] one page modified --- deep-learning-1.md | 90 +++++++++++++++------------------------------- 1 file changed, 28 insertions(+), 62 deletions(-) diff --git a/deep-learning-1.md b/deep-learning-1.md index 40ba7d5..20578a7 100644 --- a/deep-learning-1.md +++ b/deep-learning-1.md @@ -18,43 +18,30 @@ * From old to state-of-the-art * from generic to specific areas * focus on state-of-the-art -* [Lots of Deep Learning Resourc](http://deeplearning4j.org/documentation.html)[Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Webs](http://cs224d.stanford.edu/)[Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) -* [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) -* [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) -* [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) -* [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) -* [Recent Reddit AMAs related to Deep Learning](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [Another AMA](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/) -* [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) -* [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) -* [Introduction to Deep Learning Using Python \(GitHub\)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) -* [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) -* [Deep Learning Software List](http://deeplearning.net/software_links/) -* [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) -* [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) -* [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) -* [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) -* [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) -* [deeplearning Tutorials](http://deeplearning4j.org/) -* [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) -* [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) -* [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) -* [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) -* [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) -* [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) -* [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) -* [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) -* [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) +* [Lots of Deep Learning Resourc](http://deeplearning4j.org/documentation.html)[Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Webs](http://cs224d.stanford.edu/)[Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) Parallel Forall that aims to provide an intuitive and gentle introduction to[ deep learning](https://developer.nvidia.com/deep-learning). +* [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. +* [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) his tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. +* [Deep Learning FAQs on Quor](https://www.quora.com/topic/Deep-Learning/faq)​[Google+ Deep Learning ](https://plus.google.com/communities/112866381580457264725)[Where to Learn Deep Learning](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning. +* [Introduction to Deep Learning Using Python \(GitHub\)](https://github.com/rouseguy/intro2deeplearning), Introduction to Deep Learning and resources to get started +* [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/)[Deep Learning Software List](http://deeplearning.net/software_links/)[Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field. +* [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA)​[Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) +* [deeplearning Tutorials](http://deeplearning4j.org/) Open-source, distributed, deep learning library for the JVM +* [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) This tutorial will introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java. +* [Deep Learning Basic](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)[Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) Breaking down Neural Networks: An intuitive approach to Backpropagation. +* [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) Multi-Layer Neural Network. +* [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) Difference between train, validation and test set, in neural networks. +* [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) Good resources for learning about Artificial Neural Networks. +* [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) Network structure inspired by simplified models of biological neurons \(brain cells\). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof. +* [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html)​[Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) _Neural Networks and Deep Learning_ is a free online book. The book will teach you about: + * Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data + * Deep learning, a powerful set of techniques for learning in neural networks * Neural Machine Translation - * [**Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list) - * [Introduction to Neural Machine Translation with GPUs \(part 1\)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) - * [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) - - - + * [**Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list) ****This is a machine translation reading list maintained by the Tsinghua Natural Language Processing Group. + * [Introduction to Neural Machine Translation with GPUs \(part 1\)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) Neural machine translation is a recently proposed framework for machine translation based purely on neural networks + * [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learni](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/). ​ * Deep Learning Frameworks - * * [Torch vs. Theano](http://fastml.com/torch-vs-theano/) - * [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) - * [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) + * [Torch vs. Theano](http://fastml.com/torch-vs-theano/) Recently we took a look at [Torch 7](http://fastml.com/loading-data-in-torch-is-a-mess/) and found its data ingestion facilities less than impressive. Torch’s biggest competitor seems to be [Theano](http://deeplearning.net/software/theano/index.html), a popular deep-learning framework for Python. + * [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html)​[Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) Curated list of deep Learning Libraries by Language. * [Theano](https://en.wikipedia.org/wiki/Theano_%28software%29) * [Website](http://deeplearning.net/software/theano/) * [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) @@ -94,11 +81,8 @@ * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) * [Creating Custom Model For Android Using TensorFlow](https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb) * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample) - - - * Feed Forward Networks - * * [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) + * [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) * [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) * [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) * [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) @@ -110,11 +94,8 @@ * [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) * [Regression and Classification with NNs \(Slides\)](http://www.autonlab.org/tutorials/neural13.pdf) * [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) - - - * Recurrent and LSTM Networks - * * [awesome-rnn: list of resources \(GitHub Repo\)](https://github.com/kjw0612/awesome-rnn) + * [awesome-rnn: list of resources \(GitHub Repo\)](https://github.com/kjw0612/awesome-rnn) * [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) * [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) * [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) @@ -144,36 +125,24 @@ * Gated Recurrent Units \(GRU\) * [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) * [Time series forecasting with Sequence-to-Sequence \(seq2seq\) rnn models](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) - - - * [Recursive Neural Network \(not Recurrent\)](https://en.wikipedia.org/wiki/Recursive_neural_network) * [Recursive Neural Tensor Network \(RNTN\)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - - - * Restricted Boltzmann Machine - * * [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) + * [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) * [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) * [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) * [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) * [RBMs in R](https://github.com/zachmayer/rbm) * [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) - - - * Autoencoders: Unsupervised \(applies BackProp after setting target = input\) - * * [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) + * [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) * [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) * [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) * [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) - - - * Convolutional Neural Networks - * * [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) + * [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) * [Awesome Deep Vision: List of Resources \(GitHub\)](https://github.com/kjw0612/awesome-deep-vision) * [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) * [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) @@ -183,11 +152,8 @@ * [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) * [Interview with Yann LeCun \| Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) * [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) - - - * Network Representation Learning - * * [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) + * [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) * [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) * [Network Representation Learning Papers](https://github.com/thunlp) * [Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) From bbfd4046d0312118e8b8e9d4262b9cebbbb6ffb3 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 15:24:04 +0000 Subject: [PATCH 10/20] GitBook: [master] 2 pages modified --- deep-learning-1.md | 125 ++++++++++++++++++++++++++--------------- interview-resources.md | 11 ++-- 2 files changed, 84 insertions(+), 52 deletions(-) diff --git a/deep-learning-1.md b/deep-learning-1.md index 20578a7..6aac458 100644 --- a/deep-learning-1.md +++ b/deep-learning-1.md @@ -1,47 +1,59 @@ # Deep Learning -* [Complete Guide to TensorFlow for Deep Learning with Python](https://career.guru99.com/recommends/deeplearning-2/) Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! -* [Deep Learning Specialization](https://career.guru99.com/recommends/deeplearning-1/) If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. -* [Deep Learning with Python and Keras](https://career.guru99.com/recommends/deeplearning-3/) Understand and build Deep Learning models for images, text and more using Python and Keras -* [Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs](https://career.guru99.com/recommends/deeplearning-4/) Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. -* [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://career.guru99.com/recommends/deeplearning-5/) Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. -* [Natural Language Processing with Deep Learning in Python](https://career.guru99.com/recommends/deeplearning-6/) Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets. -* [Deep Learning by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai2&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) Become a Deep Learning experts. Master Deep Learning and Break into AI. -* [IBM Data Science Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai12&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fibm-data-science-professional-certificate) Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required. -* [Deep Learning by IBM \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai17&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fibm-deep-learning) This program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning. -* [Data Science by Harvard University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai18&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fharvardx-data-science) Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem. -* [Microsoft Professional Program in Data Science \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai19&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-data-science) Microsoft courses found here can be audited free or students can choose to receive a verified certificate for a small fee. -* [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) This course is to make deep learning accessible to as many people as possible. The only prerequisite is that you know how to code \(a year of experience is enough\), preferably in Python, and that you have at least followed a high school math course. -* [fast.ai - Cutting Edge Deep Learning For C](http://course.fast.ai/part2.html)[A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) A curated list of awesome Deep Learning tutorials, projects and communities. -* [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md) The roadmap is constructed in accordance with the following four guidelines: - * From outline to detail - * From old to state-of-the-art - * from generic to specific areas - * focus on state-of-the-art -* [Lots of Deep Learning Resourc](http://deeplearning4j.org/documentation.html)[Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Webs](http://cs224d.stanford.edu/)[Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) Parallel Forall that aims to provide an intuitive and gentle introduction to[ deep learning](https://developer.nvidia.com/deep-learning). -* [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. -* [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) his tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. -* [Deep Learning FAQs on Quor](https://www.quora.com/topic/Deep-Learning/faq)​[Google+ Deep Learning ](https://plus.google.com/communities/112866381580457264725)[Where to Learn Deep Learning](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning. -* [Introduction to Deep Learning Using Python \(GitHub\)](https://github.com/rouseguy/intro2deeplearning), Introduction to Deep Learning and resources to get started -* [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/)[Deep Learning Software List](http://deeplearning.net/software_links/)[Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field. -* [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA)​[Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) -* [deeplearning Tutorials](http://deeplearning4j.org/) Open-source, distributed, deep learning library for the JVM -* [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) This tutorial will introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java. -* [Deep Learning Basic](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)[Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) Breaking down Neural Networks: An intuitive approach to Backpropagation. -* [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) Multi-Layer Neural Network. -* [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) Difference between train, validation and test set, in neural networks. -* [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) Good resources for learning about Artificial Neural Networks. -* [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) Network structure inspired by simplified models of biological neurons \(brain cells\). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof. -* [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html)​[Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) _Neural Networks and Deep Learning_ is a free online book. The book will teach you about: - * Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data - * Deep learning, a powerful set of techniques for learning in neural networks +* [Complete Guide to TensorFlow for Deep Learning with Python](https://career.guru99.com/recommends/deeplearning-2/) +* [Deep Learning Specialization](https://career.guru99.com/recommends/deeplearning-1/) +* [Deep Learning with Python and Keras](https://career.guru99.com/recommends/deeplearning-3/) +* [Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs](https://career.guru99.com/recommends/deeplearning-4/) +* [Deep Learning A-Z™: Hands-On Artificial Neural Networks](https://career.guru99.com/recommends/deeplearning-5/) +* [Natural Language Processing with Deep Learning in Python](https://career.guru99.com/recommends/deeplearning-6/) +* [Deep Learning by Andrew Ng \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai2&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning) +* [IBM Data Science Professional Certificate \(Coursera\)](https://click.linksynergy.com/deeplink?id=vedj0cWlu2Y&mid=40328&u1=ddai12&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fibm-data-science-professional-certificate) +* [Deep Learning by IBM \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai17&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fibm-deep-learning) +* [Data Science by Harvard University \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai18&p=https%3A%2F%2Fwww.edx.org%2Fprofessional-certificate%2Fharvardx-data-science) +* [Microsoft Professional Program in Data Science \(edX\)](https://www.awin1.com/cread.php?awinmid=6798&awinaffid=427859&clickref=ddai19&p=https%3A%2F%2Fwww.edx.org%2Fmicrosoft-professional-program-data-science) +* [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) +* [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html) +* [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) +* [**Deep Learning Papers Reading Roadmap**](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md) +* [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) +* [Interesting Deep Learning and NLP Projects \(Stanford\)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) +* [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +* [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) +* [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) +* [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) +* [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) +* [Recent Reddit AMAs related to Deep Learning](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [Another AMA](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/) +* [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) +* [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) +* [Introduction to Deep Learning Using Python \(GitHub\)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) +* [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) +* [Deep Learning Software List](http://deeplearning.net/software_links/) +* [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) +* [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) +* [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) +* [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) +* [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) +* [deeplearning Tutorials](http://deeplearning4j.org/) +* [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) +* [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) +* [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) +* [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) +* [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ) +* [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) +* [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) +* [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) +* [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) * Neural Machine Translation - * [**Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list) ****This is a machine translation reading list maintained by the Tsinghua Natural Language Processing Group. - * [Introduction to Neural Machine Translation with GPUs \(part 1\)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) Neural machine translation is a recently proposed framework for machine translation based purely on neural networks - * [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learni](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/). ​ + * [**Machine Translation Reading List**](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list) + * [Introduction to Neural Machine Translation with GPUs \(part 1\)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) + * [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) + + + * Deep Learning Frameworks - * [Torch vs. Theano](http://fastml.com/torch-vs-theano/) Recently we took a look at [Torch 7](http://fastml.com/loading-data-in-torch-is-a-mess/) and found its data ingestion facilities less than impressive. Torch’s biggest competitor seems to be [Theano](http://deeplearning.net/software/theano/index.html), a popular deep-learning framework for Python. - * [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html)​[Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) Curated list of deep Learning Libraries by Language. + * * [Torch vs. Theano](http://fastml.com/torch-vs-theano/) + * [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) + * [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) * [Theano](https://en.wikipedia.org/wiki/Theano_%28software%29) * [Website](http://deeplearning.net/software/theano/) * [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) @@ -81,8 +93,11 @@ * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) * [Creating Custom Model For Android Using TensorFlow](https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb) * [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample) + + + * Feed Forward Networks - * [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) + * * [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) * [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) * [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) * [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) @@ -94,8 +109,11 @@ * [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) * [Regression and Classification with NNs \(Slides\)](http://www.autonlab.org/tutorials/neural13.pdf) * [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) + + + * Recurrent and LSTM Networks - * [awesome-rnn: list of resources \(GitHub Repo\)](https://github.com/kjw0612/awesome-rnn) + * * [awesome-rnn: list of resources \(GitHub Repo\)](https://github.com/kjw0612/awesome-rnn) * [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) * [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) * [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) @@ -125,24 +143,36 @@ * Gated Recurrent Units \(GRU\) * [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) * [Time series forecasting with Sequence-to-Sequence \(seq2seq\) rnn models](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) + + + * [Recursive Neural Network \(not Recurrent\)](https://en.wikipedia.org/wiki/Recursive_neural_network) * [Recursive Neural Tensor Network \(RNTN\)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + + + * Restricted Boltzmann Machine - * [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) + * * [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) * [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) * [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) * [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) * [RBMs in R](https://github.com/zachmayer/rbm) * [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) * [word2vec, DBN, RNTN for Sentiment Analysis](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) + + + * Autoencoders: Unsupervised \(applies BackProp after setting target = input\) - * [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) + * * [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) * [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) * [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) * [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) + + + * Convolutional Neural Networks - * [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) + * * [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) * [Awesome Deep Vision: List of Resources \(GitHub\)](https://github.com/kjw0612/awesome-deep-vision) * [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) * [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) @@ -152,8 +182,11 @@ * [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) * [Interview with Yann LeCun \| Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) * [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) + + + * Network Representation Learning - * [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) + * * [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) * [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) * [Network Representation Learning Papers](https://github.com/thunlp) * [Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) diff --git a/interview-resources.md b/interview-resources.md index f36d51b..b65aba0 100644 --- a/interview-resources.md +++ b/interview-resources.md @@ -1,9 +1,8 @@ # Interview Resources -* [41 Essential Machine Learning Interview Questions \(with answers\)](https://www.springboard.com/blog/machine-learning-interview-questions/) -* [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) -* [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) -* [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) -* [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) -* [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63) +* [51 Essential Machine Learning Interview Questions \(with answers\)](https://www.springboard.com/blog/machine-learning-interview-questions/) Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer, or data engineer. +* Quora Questions and answers: + * [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) + * [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) + * [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq)[What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) From b4707d8e6c84a07fbd2960dd053cf80324577813 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 15:27:08 +0000 Subject: [PATCH 11/20] GitBook: [master] 2 pages modified --- genetic-algorithms.md | 12 ++++++------ interview-resources.md | 11 ++++++----- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/genetic-algorithms.md b/genetic-algorithms.md index abdd509..c56821e 100644 --- a/genetic-algorithms.md +++ b/genetic-algorithms.md @@ -1,10 +1,10 @@ # Genetic Algorithms -* [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) -* [Simple Implementation of Genetic Algorithms in Python \(Part 1\)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) -* [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) -* [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) +* [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) Genetic Algorithm explained from scratch by wikipedia. +* [Simple Implementation of Genetic Algorithms in Python \(Part 1\)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) A simple yet powerful genetic algorithm implementation used to train a neural network in 15 lines of code. +* [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) When should I use genetic algorithms as opposed to neural networks?. +* [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) The aim of this tutorial is to explain genetic algorithms sufficiently for you to be able to use them in your own projects. * [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) - * [Genetic Programming in Python \(GitHub\)](https://github.com/trevorstephens/gplearn) - * [Genetic Alogorithms vs Genetic Programming \(Quora\)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) + * [Genetic Programming in Python \(GitHub\)](https://github.com/trevorstephens/gplearn) Genetic Programming in Python, with a scikit-learn inspired API. + * [Genetic Alogorithms vs Genetic Programming \(Quora\)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) What's the difference between Genetic Algorithms and Genetic Programming?. diff --git a/interview-resources.md b/interview-resources.md index b65aba0..f36d51b 100644 --- a/interview-resources.md +++ b/interview-resources.md @@ -1,8 +1,9 @@ # Interview Resources -* [51 Essential Machine Learning Interview Questions \(with answers\)](https://www.springboard.com/blog/machine-learning-interview-questions/) Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer, or data engineer. -* Quora Questions and answers: - * [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) - * [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) - * [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq)[What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) +* [41 Essential Machine Learning Interview Questions \(with answers\)](https://www.springboard.com/blog/machine-learning-interview-questions/) +* [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews) +* [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) +* [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) +* [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) +* [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63) From 623d93d12ebe571ab3da879237f5a625a00e20e8 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 15:37:56 +0000 Subject: [PATCH 12/20] GitBook: [master] 2 pages modified --- genetic-algorithms.md | 12 ++++++------ statistics.md | 18 +++++++++--------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/genetic-algorithms.md b/genetic-algorithms.md index c56821e..abdd509 100644 --- a/genetic-algorithms.md +++ b/genetic-algorithms.md @@ -1,10 +1,10 @@ # Genetic Algorithms -* [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) Genetic Algorithm explained from scratch by wikipedia. -* [Simple Implementation of Genetic Algorithms in Python \(Part 1\)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) A simple yet powerful genetic algorithm implementation used to train a neural network in 15 lines of code. -* [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) When should I use genetic algorithms as opposed to neural networks?. -* [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) The aim of this tutorial is to explain genetic algorithms sufficiently for you to be able to use them in your own projects. +* [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) +* [Simple Implementation of Genetic Algorithms in Python \(Part 1\)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) +* [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) +* [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) * [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) - * [Genetic Programming in Python \(GitHub\)](https://github.com/trevorstephens/gplearn) Genetic Programming in Python, with a scikit-learn inspired API. - * [Genetic Alogorithms vs Genetic Programming \(Quora\)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) What's the difference between Genetic Algorithms and Genetic Programming?. + * [Genetic Programming in Python \(GitHub\)](https://github.com/trevorstephens/gplearn) + * [Genetic Alogorithms vs Genetic Programming \(Quora\)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) diff --git a/statistics.md b/statistics.md index a0d1ccc..92948a7 100644 --- a/statistics.md +++ b/statistics.md @@ -1,16 +1,16 @@ # Statistics -* [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics -* [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach -* [Statistics for Hackers \| Slides \| @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas -* [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics -* [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) +* [Stat Trek Website](http://stattrek.com/) A dedicated website to teach yourselves Statistics +* [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) Learn Statistics using an application-centric programming approach +* [Statistics for Hackers \| Slides \| @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) Slides by Jake VanderPlas +* [Online Statistics Book](http://onlinestatbook.com/2/index.html) An Interactive Multimedia Course for Studying Statistics +* [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) Sampling Distributions explained from scratch. * Tutorials * [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) * [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) * [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) -* [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) -* [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) -* [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) -* [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook +* [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) Understand the core concepts of unbiased estimator. +* [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) Goodness of fit explained from scratch in github. +* [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) QQ plots explained using constructive diagrams. +* [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) Free PDF textbook From 591a91ffe0fc0770846224d99e56c3e45251f7b0 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 15:43:38 +0000 Subject: [PATCH 13/20] GitBook: [master] 2 pages modified --- cheat-sheets.md | 6 +++--- statistics.md | 18 +++++++++--------- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/cheat-sheets.md b/cheat-sheets.md index decd51c..dc88730 100644 --- a/cheat-sheets.md +++ b/cheat-sheets.md @@ -1,6 +1,6 @@ # Cheat Sheets -* [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) -* [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) -* [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) +* [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) A 10 page cheatsheet on the best use cases of probability in AI/ML. +* [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) Classical equations and diagrams in machine learning. +* [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) Quick definitions and intuitive explanations around machine learning. diff --git a/statistics.md b/statistics.md index 92948a7..a0d1ccc 100644 --- a/statistics.md +++ b/statistics.md @@ -1,16 +1,16 @@ # Statistics -* [Stat Trek Website](http://stattrek.com/) A dedicated website to teach yourselves Statistics -* [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) Learn Statistics using an application-centric programming approach -* [Statistics for Hackers \| Slides \| @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) Slides by Jake VanderPlas -* [Online Statistics Book](http://onlinestatbook.com/2/index.html) An Interactive Multimedia Course for Studying Statistics -* [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) Sampling Distributions explained from scratch. +* [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics +* [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach +* [Statistics for Hackers \| Slides \| @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas +* [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics +* [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) * Tutorials * [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) * [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) * [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) -* [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) Understand the core concepts of unbiased estimator. -* [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) Goodness of fit explained from scratch in github. -* [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) QQ plots explained using constructive diagrams. -* [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) Free PDF textbook +* [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) +* [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) +* [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) +* [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook From c687caaadda467c6ba7df1f26a09b60cf6600999 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 16:01:21 +0000 Subject: [PATCH 14/20] GitBook: [master] 2 pages modified --- cheat-sheets.md | 6 +++--- natural-language-processing.md | 26 +++++++++++++------------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/cheat-sheets.md b/cheat-sheets.md index dc88730..decd51c 100644 --- a/cheat-sheets.md +++ b/cheat-sheets.md @@ -1,6 +1,6 @@ # Cheat Sheets -* [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) A 10 page cheatsheet on the best use cases of probability in AI/ML. -* [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) Classical equations and diagrams in machine learning. -* [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) Quick definitions and intuitive explanations around machine learning. +* [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) +* [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) +* [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) diff --git a/natural-language-processing.md b/natural-language-processing.md index 73d5ec1..9f179b8 100644 --- a/natural-language-processing.md +++ b/natural-language-processing.md @@ -2,17 +2,17 @@ -* [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) -* [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) -* [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) -* [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) -* [The Stanford NLP Group](https://nlp.stanford.edu/) -* [NLP from Scratch \| Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) -* [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) +* [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) A curated list of speech and natural language processing resources that can be useful for beginners. +* [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. +* [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) What is TF-IDF? The 10 minute guide +* [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) Deep Learning for Natural Language Processing +* [The Stanford NLP Group](https://nlp.stanford.edu/) The Natural Language Processing Group at Stanford University is a team of faculty, postdocs, programmers and students who work together on algorithms that allow computers to process and understand human languages. +* [NLP from Scratch \| Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) Natural Language Processing \(almost\) from Scratch +* [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) Graph-based Semi-Supervised Learning Algorithms for NLP * [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) - * [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) -* * Topic Modeling - * * [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) + * [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) +* Topic Modeling + * [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) * [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) * [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) * [What is a good explanation of Latent Dirichlet Allocation \(LDA\)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) @@ -34,9 +34,9 @@ * [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) * Python * [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) - * [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) -* * word2vec - * * [Google word2vec](https://code.google.com/archive/p/word2vec) + * [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) +* word2vec + * [Google word2vec](https://code.google.com/archive/p/word2vec) * [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) * [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) * [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) From b8ab5d46aee99189cc8ada31510a8f3674283bfd Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Thu, 21 Jan 2021 16:10:10 +0000 Subject: [PATCH 15/20] GitBook: [master] 2 pages modified --- extra.md | 46 ++++++++++++++++------------------ natural-language-processing.md | 26 +++++++++---------- 2 files changed, 35 insertions(+), 37 deletions(-) diff --git a/extra.md b/extra.md index b9769df..648191c 100644 --- a/extra.md +++ b/extra.md @@ -1,27 +1,25 @@ # Extra -* [Machine Learning Course by Andrew Ng \(Stanford University\)](https://www.coursera.org/learn/machine-learning) -* [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) -* [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) -* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) -* [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) -* [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) -* [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) -* [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) -* [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) -* [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) -* [The Open Source Data Science Masters](http://datasciencemasters.org/) -* [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) -* [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) -* [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) -* [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) -* [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) -* [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) -* [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) -* [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) -* [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) -* [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) -* [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) -* [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) -* [Twitter's Most Shared \#machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) +* [Machine Learning Course by Andrew Ng \(Stanford University\)](https://www.coursera.org/learn/machine-learning) This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: \(i\) Supervised learning \(parametric/non-parametric algorithms, support vector machines, kernels, neural networks\). \(ii\) Unsupervised learning \(clustering, dimensionality reduction, recommender systems, deep learning\). \(iii\) Best practices in machine learning. +* [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) Learn Machine Learning online from the best machine learning courses/tutorials submitted & voted by the programming community. +* [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) Introduction in-depth to machine learning in 15 hours of expert videos by experts. +* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) A book by Gareth James which will explain about statistical learning. +* [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) List of awesome university courses for learning Computer Science!. +* [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) A complete daily plan for studying to become a machine learning engineer. +* [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) Dive into Machine Learning with Python Jupyter notebook and scikit-learn! +* [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) A curated list of awesome Machine Learning frameworks, libraries and software. +* [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) A curated list of awesome data visualization libraries and resources. +* [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) An awesome Data Science repository to learn and apply for real world problems. +* [The Open Source Data Science Masters](http://datasciencemasters.org/) The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to making use of data. +* [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) Machine learning algorithms build a model of the training data. +* [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) What are some machine learning algorithms that you should always have a strong understanding of, and why? +* [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf)​[List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) Machine Learning concepts explained from scratch. +* [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) Machine Learning lecture slides from MIT. +* [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning. +* [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) This post is a collection of resources that I found particularly useful when I was learning the fundamentals of data science. +* [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) New to Machine Learning? Avoid these three mistakes. +* [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) Statistical Methods for Machine Learning which can be useful. +* [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) Notes from the edX Course +* [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks. +* [Twitter's Most Shared \#machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) Highest ranked \#machinelearning content from the past 7 Days diff --git a/natural-language-processing.md b/natural-language-processing.md index 9f179b8..73d5ec1 100644 --- a/natural-language-processing.md +++ b/natural-language-processing.md @@ -2,17 +2,17 @@ -* [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) A curated list of speech and natural language processing resources that can be useful for beginners. -* [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. -* [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) What is TF-IDF? The 10 minute guide -* [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) Deep Learning for Natural Language Processing -* [The Stanford NLP Group](https://nlp.stanford.edu/) The Natural Language Processing Group at Stanford University is a team of faculty, postdocs, programmers and students who work together on algorithms that allow computers to process and understand human languages. -* [NLP from Scratch \| Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) Natural Language Processing \(almost\) from Scratch -* [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) Graph-based Semi-Supervised Learning Algorithms for NLP +* [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) +* [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) +* [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) +* [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) +* [The Stanford NLP Group](https://nlp.stanford.edu/) +* [NLP from Scratch \| Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) +* [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) * [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) - * [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) -* Topic Modeling - * [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) + * [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) +* * Topic Modeling + * * [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) * [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) * [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) * [What is a good explanation of Latent Dirichlet Allocation \(LDA\)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) @@ -34,9 +34,9 @@ * [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) * Python * [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) - * [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) -* word2vec - * [Google word2vec](https://code.google.com/archive/p/word2vec) + * [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) +* * word2vec + * * [Google word2vec](https://code.google.com/archive/p/word2vec) * [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) * [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) * [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) From 7bb4ff17d3d1ec20d7052d21c1e1e7c9867b1e92 Mon Sep 17 00:00:00 2001 From: Priyanka Kasture <42208426+priyanka-kasture@users.noreply.github.com> Date: Fri, 22 Jan 2021 21:06:20 +0530 Subject: [PATCH 16/20] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cd4ffb4..2a4e2d9 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ description: >- ## About Us -Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving community of over **350,000** ardent - artificial intelligence and machine learning enthusiasts across India and the globe. We at MLI, believe that India has the potential to position itself among leaders, on the global technology map. +Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving community of over **400,000** ardent - artificial intelligence and machine learning enthusiasts across India and the globe. We at MLI, believe that India has the potential to position itself among leaders, on the global technology map. The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI ecosystem and talent pool; thereby leading our country to have a significant take in the global AI revolution_. To pursue the same, we intend to democratize quality **technical education**, **resources,** and **opportunities** and make them available to all. From 6be08fbc7ff6e032c87c602aef3cdb70090032b3 Mon Sep 17 00:00:00 2001 From: Allen Joseph Date: Sat, 23 Jan 2021 08:27:09 +0000 Subject: [PATCH 17/20] GitBook: [master] one page modified --- README.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 2a4e2d9..d3877ff 100644 --- a/README.md +++ b/README.md @@ -9,10 +9,17 @@ description: >- ## About Us -Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving community of over **400,000** ardent - artificial intelligence and machine learning enthusiasts across India and the globe. We at MLI, believe that India has the potential to position itself among leaders, on the global technology map. - +Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving community of over **400,000** ardent - artificial intelligence and machine learning enthusiasts across India and the globe. We at MLI, believe that India has the potential to position itself among leaders, on the global technology map. + The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI ecosystem and talent pool; thereby leading our country to have a significant take in the global AI revolution_. To pursue the same, we intend to democratize quality **technical education**, **resources,** and **opportunities** and make them available to all. +## Social Media Links + +* Instagram: [https://www.instagram.com/ml.india/](https://www.instagram.com/ml.india/) +* Linkedln: [https://www.linkedin.com/company/mlindia/](https://www.linkedin.com/company/mlindia/) +* Twitter: [https://twitter.com/ml\_india\_](https://twitter.com/ml_india_) +* Youtube: [https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ](https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ) + ## Table of Contents {% page-ref page="./" %} @@ -37,5 +44,3 @@ The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI e {% page-ref page="extra.md" %} - - From ad3959da6590f62d8a35771bc59160ad3d64de8b Mon Sep 17 00:00:00 2001 From: Priyanka Kasture <42208426+priyanka-kasture@users.noreply.github.com> Date: Sun, 24 Jan 2021 17:40:15 +0530 Subject: [PATCH 18/20] Update README.md --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index d3877ff..c332c73 100644 --- a/README.md +++ b/README.md @@ -15,10 +15,12 @@ The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI e ## Social Media Links -* Instagram: [https://www.instagram.com/ml.india/](https://www.instagram.com/ml.india/) -* Linkedln: [https://www.linkedin.com/company/mlindia/](https://www.linkedin.com/company/mlindia/) -* Twitter: [https://twitter.com/ml\_india\_](https://twitter.com/ml_india_) -* Youtube: [https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ](https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ) +* Follow us on Instagram for ML/AI based infographics: [https://www.instagram.com/ml.india/](https://www.instagram.com/ml.india/) +* Follow us on Linkedln to stay updated about our events and career opportunities: [https://www.linkedin.com/company/mlindia/](https://www.linkedin.com/company/mlindia/) +* Follow us on Twitter for latest news from the industry: [https://twitter.com/ml\_india\_](https://twitter.com/ml_india_) +* Learn, share and network with lik-eminded AI/ML enthusiasts on our community platform: [https://mlindia.mn.co_](https://mlindia.mn.co) +* Join our 10,000 strong monthly-newsletter family: [https://bit.ly/mli-newsletter_](https://bit.ly/mli-newsletter) +* Our Youtube Channel: [https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ](https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ) ## Table of Contents From b598f41fcc8b6e0bf85cc7ebf31e7b818a8633cc Mon Sep 17 00:00:00 2001 From: Priyanka Kasture <42208426+priyanka-kasture@users.noreply.github.com> Date: Sun, 24 Jan 2021 17:41:00 +0530 Subject: [PATCH 19/20] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c332c73..532f3e5 100644 --- a/README.md +++ b/README.md @@ -18,8 +18,8 @@ The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI e * Follow us on Instagram for ML/AI based infographics: [https://www.instagram.com/ml.india/](https://www.instagram.com/ml.india/) * Follow us on Linkedln to stay updated about our events and career opportunities: [https://www.linkedin.com/company/mlindia/](https://www.linkedin.com/company/mlindia/) * Follow us on Twitter for latest news from the industry: [https://twitter.com/ml\_india\_](https://twitter.com/ml_india_) -* Learn, share and network with lik-eminded AI/ML enthusiasts on our community platform: [https://mlindia.mn.co_](https://mlindia.mn.co) -* Join our 10,000 strong monthly-newsletter family: [https://bit.ly/mli-newsletter_](https://bit.ly/mli-newsletter) +* Learn, share and network with lik-eminded AI/ML enthusiasts on our community platform: [https://mlindia.mn.co](https://mlindia.mn.co) +* Join our 10,000 strong monthly-newsletter family: [https://bit.ly/mli-newsletter](https://bit.ly/mli-newsletter) * Our Youtube Channel: [https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ](https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ) ## Table of Contents From b0270bed7331f5f06fcdc14dc96dcd48f0b4b17c Mon Sep 17 00:00:00 2001 From: Priyanka Kasture <42208426+priyanka-kasture@users.noreply.github.com> Date: Sun, 24 Jan 2021 17:42:27 +0530 Subject: [PATCH 20/20] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 532f3e5..a83a1a1 100644 --- a/README.md +++ b/README.md @@ -13,13 +13,13 @@ Founded in April of 2018, **Machine Learning India \(MLI\)**, is a thriving comm The goal of MLI is _to reduce the skill-gap in India, by creating a vibrant AI ecosystem and talent pool; thereby leading our country to have a significant take in the global AI revolution_. To pursue the same, we intend to democratize quality **technical education**, **resources,** and **opportunities** and make them available to all. -## Social Media Links +## Our Social Media Links * Follow us on Instagram for ML/AI based infographics: [https://www.instagram.com/ml.india/](https://www.instagram.com/ml.india/) * Follow us on Linkedln to stay updated about our events and career opportunities: [https://www.linkedin.com/company/mlindia/](https://www.linkedin.com/company/mlindia/) * Follow us on Twitter for latest news from the industry: [https://twitter.com/ml\_india\_](https://twitter.com/ml_india_) -* Learn, share and network with lik-eminded AI/ML enthusiasts on our community platform: [https://mlindia.mn.co](https://mlindia.mn.co) -* Join our 10,000 strong monthly-newsletter family: [https://bit.ly/mli-newsletter](https://bit.ly/mli-newsletter) +* Learn, share and network with like-minded AI/ML enthusiasts on our community platform: [https://mlindia.mn.co](https://mlindia.mn.co) +* Join our 10,000 strong monthly-newsletter subscriber family: [https://bit.ly/mli-newsletter](https://bit.ly/mli-newsletter) * Our Youtube Channel: [https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ](https://www.youtube.com/channel/UCaKsxDijTJoXDMIgAuNYfcQ) ## Table of Contents