diff --git a/README.md b/README.md index b60b94e..a83a1a1 100644 --- a/README.md +++ b/README.md @@ -1 +1,48 @@ -# 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 **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. + +## 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 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 + +{% 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..c61f428 --- /dev/null +++ b/artificial-intelligence.md @@ -0,0 +1,41 @@ +# Artificial Intelligence + +* [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/) 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/)​ +* ​[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..648191c --- /dev/null +++ b/extra.md @@ -0,0 +1,25 @@ +# Extra + +* [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/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 +