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Deep Learning with TensorFlow and PyTorch

Overview

This repository showcases implementations of various deep learning neural network architectures using TensorFlow and PyTorch frameworks. The aim is to provide a comprehensive guide for learning and understanding deep learning concepts by comparing these two popular frameworks.

Table of Contents

• Prerequisites
• Installation
• Implemented Models
• Usage
• References
• Contributing

Prerequisites

Before using this repository, make sure you have the following installed:

• Python 3.x
• TensorFlow
• PyTorch
• NumPy
• Matplotlib
• Other libraries listed in requirements.txt

Installation

To get started, clone the repository and install the required dependencies:

git clone https://github.com/MDalamin5/DeepLearning-With-Tensorflow-and-Pytorch
cd repo-name
pip install -r requirements.txt

Implemented Models

The repository contains implementations of the following neural networks:

1. Feedforward Neural Networks (FNN)
   • Implementations using both TensorFlow and PyTorch
2. Convolutional Neural Networks (CNN)
   • For image classification tasks
3. Recurrent Neural Networks (RNN)
   • Focused on sequential data processing
4. Long Short-Term Memory (LSTM)
   • Dealing with long-term dependencies in sequences
5. Generative Adversarial Networks (GAN)
   • Generating synthetic data from noise
6. Transfer Learning
   • Using pre-trained models for new tasks
7. Reinforcement Learning
   • Neural networks for policy learning

Usage

Each model comes with detailed Jupyter notebooks for training, evaluation, and experimentation. To run a specific model:

1. Navigate to the respective directory for TensorFlow or PyTorch.
2. Run the provided Python scripts or open the corresponding Jupyter notebook.

For example, to run the TensorFlow-based CNN implementation:

cd tensorflow/cnn
python train_cnn.py

For PyTorch-based RNN:

cd pytorch/rnn
python train_rnn.py

References

The implementations are inspired by the following resources:

• Deep Learning with Python by François Chollet
• PyTorch Documentation
• TensorFlow Tutorials

Contributing

Contributions are welcome! Please fork the repository, create a new branch, and submit a pull request. Make sure your code is well-documented and adheres to the existing style.

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