🧠 Deep Learning Repository

A comprehensive collection of deep learning implementations, tutorials, and advanced architectures

🚀 Getting Started • 📚 Documentation • 🏗️ Architecture • 🤝 Contributing

---

📋 Table of Contents

• 🎯 Overview
• 🚀 Getting Started
• 📁 Repository Structure
  • 🔥 PyTorch Implementations
  • 🏗️ Special Architectures
  • ⚡ TensorFlow Advanced
  • 📚 TensorFlow Basics
• 💻 Installation
• 🎓 Usage Examples
• 📖 Learning Path
• 🤝 Contributing
• 📄 License

🎯 Overview

This repository contains a comprehensive collection of deep learning implementations spanning multiple frameworks and architectures. From fundamental concepts to cutting-edge research implementations, this repository serves as both an educational resource and a practical toolkit for deep learning practitioners.

✨ Key Features

• 🔬 Research-Grade Implementations: State-of-the-art architectures with detailed explanations
• 📚 Educational Content: Step-by-step tutorials and lecture materials
• 🛠️ Multiple Frameworks: PyTorch and TensorFlow implementations
• 🎯 Practical Examples: Real-world applications and use cases
• 📊 Comprehensive Coverage: From basic MLPs to advanced transformers

🚀 Getting Started

Prerequisites

• Python 3.7+
• CUDA-compatible GPU (recommended)
• 8GB+ RAM

Quick Start

# Clone the repository
git clone https://github.com/yourusername/deep-learning-repo.git
cd deep-learning-repo

# Install dependencies
pip install -r requirements.txt

# Run a quick example
cd Pytorch/MLP/1_Simple_f_approx
python simple_function_approximation.py

📁 Repository Structure

🔥 PyTorch Implementations

Complete PyTorch ecosystem with implementations ranging from basics to advanced concepts.

📊 Convolutional Neural Networks

• Image Classification - CIFAR-10, ImageNet-style classifiers
• Text Classification 1D - 1D CNNs for NLP tasks

🧠 Multi-Layer Perceptrons

• Function Approximation - Basic neural network fundamentals
• XOR Problem - Classic non-linear classification
• Image Classification - MNIST and CIFAR-10 with MLPs
• Text Classification - NLP with fully connected networks
• Sacred Experiments - Experiment tracking and management

🔄 Recurrent Neural Networks

• Signal Approximation - Time series modeling with RNNs
• Text Classification - Sentiment analysis with LSTM/GRU
  • SST-2 Dataset - Stanford Sentiment Treebank

🎓 Lecture Materials

Comprehensive PyTorch tutorials and educational content:

• PyTorch Basics - Tensors, autograd, and fundamentals
• Neural Network Modules - Building blocks of PyTorch
• Linear Regression - From scratch implementation
• Logistic Regression - Classification fundamentals
• MNIST MLP - Handwritten digit recognition
• Convolutional MNIST - CNN for image classification
• RNN Fundamentals - Sequence modeling basics
• Model Visualization - Understanding network behavior
• Overfitting & Autoencoders - Regularization techniques
• DataLoader - Efficient data handling

🏗️ Special Architectures

Advanced and cutting-edge deep learning architectures with detailed implementations.

🤖 Generative Adversarial Networks

• Simple GAN - Basic GAN implementation with Fashion-MNIST
• Advanced GAN - DCGAN, WGAN, and other variants

👁️ Vision Transformers

• From Scratch - Complete ViT implementation
• Pretrained Models - Using pretrained transformers

🏢 ResNet

Residual Networks with comprehensive documentation and multiple variants

🔗 Kolmogorov-Arnold Networks

Implementation of the novel KAN architecture

🔄 CNN + LSTM Hybrid

Combined architectures for complex pattern recognition

🌐 Sequence-to-Sequence NLP

Neural Machine Translation and other seq2seq applications

⚡ TensorFlow Advanced

Advanced TensorFlow implementations with production-ready code.

📊 Advanced CNNs

• Image Classification - Advanced CNN architectures
• Speech Recognition - 1D CNNs for audio processing

🧠 Advanced MLPs

• Function Approximation - Advanced optimization techniques
• XOR Problem - Advanced training strategies
• Image Classification - Production-ready classifiers
• Data Pipeline - Efficient data processing
• Sacred Experiments - Advanced experiment management

🔄 Advanced RNNs

• Signal Processing - Advanced time series analysis

📚 TensorFlow Basics

Foundational TensorFlow implementations perfect for beginners.

📊 Basic CNNs

• Image Classification - MNIST, CIFAR-10 basics
• Signal Classification - 1D signal processing

🧠 Basic MLPs

• Function Approximation - Neural network basics
• XOR Problem - Non-linear classification
• Image Classification - Basic image recognition
• Functional API - TensorFlow's functional approach
• Model Subclassing - Custom model creation
• Grid Search - Hyperparameter optimization

🔄 Basic RNNs

• Signal Approximation - Time series basics
• Music Classification - Audio classification

🎓 Lecture Codes

Educational materials and tutorial implementations

💻 Installation

Option 1: Conda Environment (Recommended)

# Create conda environment
conda create -n deeplearning python=3.8
conda activate deeplearning

# Install PyTorch
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

# Install TensorFlow
pip install tensorflow

# Install additional dependencies
pip install numpy pandas matplotlib seaborn scikit-learn jupyter sacred

Option 2: Virtual Environment

# Create virtual environment
python -m venv deeplearning_env
source deeplearning_env/bin/activate  # On Windows: deeplearning_env\Scripts\activate

# Install requirements
pip install torch torchvision torchaudio
pip install tensorflow
pip install numpy pandas matplotlib seaborn scikit-learn jupyter sacred

Option 3: Docker

# Pull the official TensorFlow GPU image
docker pull tensorflow/tensorflow:latest-gpu-jupyter

# Run with GPU support
docker run --gpus all -p 8888:8888 -v $(pwd):/tf/notebooks tensorflow/tensorflow:latest-gpu-jupyter

🎓 Usage Examples

Quick Start with PyTorch

# Simple MLP for MNIST
cd Pytorch/MLP/3_ImageClassification
python mnist_mlp.py

# CNN for image classification
cd Pytorch/CNN/1_ImageClassification
python cifar10_cnn.py

# RNN for text classification
cd Pytorch/RNN/2_TextClassification
python sentiment_analysis.py

Advanced Architectures

# Vision Transformer
cd Special_Architecture/Vision_Transformers/Scratch
python vision_transformer.py

# ResNet implementation
cd Special_Architecture/Resnet
python sample_resnet.py

# GAN training
cd Special_Architecture/GAN/Simple
python simple_gan.py

TensorFlow Examples

# Basic neural network
cd Tensorflow_Basic/MLP/1_Simple_f_approx
python function_approximation.py

# Advanced CNN
cd Tenflow_Advance/CNN/1_ImageClassification
python advanced_cnn.py

📖 Learning Path

🌱 Beginner Path

1. Start with basics: Tensorflow_Basic/MLP/1_Simple_f_approx
2. Learn CNNs: Tensorflow_Basic/CNN/1_ImageClassification
3. Explore RNNs: Tensorflow_Basic/RNN/1_ChirpApprox
4. PyTorch transition: Pytorch/Lecture/1-pytoch_basics

🚀 Intermediate Path

1. Advanced MLPs: Pytorch/MLP/3_ImageClassification
2. CNN architectures: Pytorch/CNN/1_ImageClassification
3. RNN applications: Pytorch/RNN/2_TextClassification
4. Experiment tracking: Pytorch/MLP/5_SacredExperiments

🎯 Advanced Path

1. Vision Transformers: Special_Architecture/Vision_Transformers
2. GANs: Special_Architecture/GAN
3. ResNet: Special_Architecture/Resnet
4. Novel architectures: Special_Architecture/KAN

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

How to Contribute

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

Contribution Areas

• 🐛 Bug fixes
• 📚 Documentation improvements
• ✨ New architecture implementations
• 🧪 Additional examples and tutorials
• 🔧 Performance optimizations

📊 Repository Statistics

• Total Implementations: 50+ neural network architectures
• Frameworks Covered: PyTorch, TensorFlow
• Application Domains: Computer Vision, NLP, Time Series, Generative Models
• Educational Content: 10+ comprehensive tutorials
• Code Quality: Documented, tested, and production-ready

🏆 Featured Implementations

• 🏗️ Vision Transformer: Complete from-scratch implementation with detailed explanations
• 🤖 ResNet: Comprehensive residual network with multiple variants
• 🎨 GANs: Simple to advanced generative models
• 🔗 KAN: Novel Kolmogorov-Arnold Networks
• 🌐 Seq2Seq: Neural machine translation systems

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: your.email@example.com

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

---

⭐ Star this repository if you find it helpful!

Made with ❤️ by the Deep Learning Community