A comprehensive PyTorch training program designed for professionals, covering from fundamentals to advanced deployment. This course provides hands-on experience with real-world applications and industry best practices.
This repository contains a complete PyTorch Deep Learning curriculum with:
- 12 Comprehensive Stages from beginner to expert level
- 40+ Specialized Modules covering all aspects of deep learning
- Real-world Projects with production-ready implementations
- Assessment Materials including quizzes and practical exercises
- Industry Applications across healthcare, finance, retail, and more
- Advanced Topics including research-oriented content
- Master PyTorch fundamentals (tensors, autograd, GPU acceleration)
- Build and train neural networks for various applications
- Implement advanced architectures (CNN, RNN, Transformers, GANs)
- Deploy models to production with modern tools and practices
- Develop end-to-end machine learning pipelines
- Module 1: Introduction to PyTorch
- Module 2: Working with Tensors
- Module 3: Automatic Differentiation
- Advanced NN Architectures - Transformers, GANs, VAEs, Attention Mechanisms
- Advanced Computer Vision - Object Detection, Image Segmentation, Modern CNNs
- Advanced NLP - BERT, GPT, Transformer-XL, Attention Mechanisms
- Research Foundations - Mathematical foundations and custom implementations
- Applied Data Science - PyTorch for data scientists
- Classical ML with PyTorch - Traditional ML algorithms in PyTorch
- Deep Learning for Business - Real-world business applications
- Industry Applications - Healthcare, Finance, Retail, Energy
- Production AI Systems - Deployment, MLOps, Monitoring
- Optimization and Deployment - Mixed Precision, Advanced Optimizers, Distributed Training, Model Compression
- Advanced Generative AI - Diffusion Models, StyleGAN, Neural Style Transfer, CLIP-Guided Generation
- Reinforcement Learning - DQN, A2C, PPO, Custom Environments
- MLOps and Experiment Tracking - MLflow, W&B, Model Registry, Deployment, Monitoring
- Self-Supervised Learning - SimCLR, MoCo, Autoencoders, Linear Evaluation
- Federated Learning - FedAvg, FedProx, Differential Privacy, Secure Aggregation
- Meta-Learning - MAML, Prototypical Networks, Relation Networks, Few-Shot Learning
- Graph Neural Networks - GCN, GAT, Graph Classification, Message Passing
- Neural Architecture Search - RL-NAS, Evolutionary NAS, DARTS, AutoML
- Multi-Modal Learning - Vision-Language Models, Cross-Modal Attention, Multi-Modal Fusion
- Model Quantization and Pruning - Dynamic/Static Quantization, Pruning Strategies, Model Compression
- Distributed Training and Scaling - Data Parallel, Model Parallel, Pipeline Parallel, Multi-Node Training
- Edge AI and Mobile Deployment - PyTorch Mobile, Android/iOS Deployment, ONNX Runtime, TensorRT
- Advanced Optimization Techniques - Lion, AdaBelief, RAdam, Advanced LR Scheduling, Lookahead
- Audio Processing - Speech Recognition, Music Generation, Real-time Audio Processing
- Time Series Analysis - Forecasting, Anomaly Detection, Sequence Modeling
- NLP Specializations - Machine Translation, Question Answering, Text Generation
- Reinforcement Learning Specializations - Multi-Agent Systems, Robotics Applications
- Edge Computing Specializations - IoT Applications, Mobile Deployment
- Advanced Research Project - Novel architectures, self-supervised learning, neural architecture search
- Production ML System - Enterprise-grade deployment, MLOps, monitoring
- Capstone Project - Multi-modal AI, real-time detection, generative AI
- Computer Vision Project - Object Detection, Image Segmentation, Generative Models
- Python 3.8+
- Basic understanding of linear algebra and calculus
- Familiarity with Python programming
- GPU (optional but recommended for advanced modules)
- Code Templates & Snippets - Reusable PyTorch patterns and templates
- Computer Vision Specializations - Medical imaging, satellite imagery, autonomous driving
# Clone the repository
git clone https://github.com/michaelgermini/PyTorch-Deep-Learning-Bootcamp.git
cd PyTorch-Deep-Learning-Bootcamp
# Install dependencies
pip install -r requirements.txt
# Or use the Makefile
make install# Build and run with Docker
docker-compose up -d
# Or use the Makefile
make docker-run# Install development dependencies
make install-dev
# Or install all dependencies including GPU support
make install-full# Install PyTorch with CUDA support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# Install GPU-specific dependencies
make install-gpuThis course prepares you for:
- PyTorch certification
- Deep learning engineering roles
- AI/ML research positions
- MLOps and deployment roles
- Module Exercises: 30% of final grade
- Mid-term Projects: 25% of final grade
- Final Capstone Project: 35% of final grade
- Participation & Engagement: 10% of final grade
- Grading Rubrics and Criteria - Comprehensive assessment guidelines
- Module Quizzes - Interactive quizzes for each stage
- Practical Exercises - Hands-on coding exercises and projects
- Clone this repository
- Install dependencies
- Start with Module 1: Introduction to PyTorch
- Complete exercises and projects in each module
- Build your portfolio with the capstone project
We welcome contributions! Please see our Contributing Guidelines for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
# Run tests
make test
# Format code
make format
# Run linting
make lint
# Build documentation
make docs
# Complete development workflow
make dev-workflowFor questions and support:
This project is licensed under the MIT License - see the LICENSE file for details.
- PyTorch team for the amazing framework
- The open-source community for inspiration and tools
- All contributors who help improve this course
⭐ If you find this course helpful, please give it a star!
Happy Learning! 🚀