This repository contains my learning and experimentation with Deep Learning concepts using Python, TensorFlow, and Keras.
The notebooks in this repository are part of my practice while studying neural networks, deep learning architectures, and model building.
The goal of this repository is to explore how deep learning models are implemented, trained, and evaluated on different types of datasets.
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Model training and evaluation
- Data preprocessing
- Working with real-world datasets
- Implementing deep learning architectures in TensorFlow/Keras
- Python
- TensorFlow
- Keras
- NumPy
- Pandas
- Matplotlib
- Jupyter Notebook
This repository is mainly for learning and practicing deep learning concepts. It includes different experiments and implementations to understand how neural network models work and how they can be applied to various problems.
As I continue learning, I plan to add more implementations such as:
- Advanced CNN architectures
- Computer Vision models
- Model optimization techniques
- Experimentation with different datasets
This repository documents my ongoing journey in Machine Learning and Deep Learning.