A comprehensive collection of PyTorch tutorials from beginner to expert level. This repository aims to provide practical, hands-on examples and explanations for various PyTorch concepts and applications.

git clone https://github.com/niconielsen32/pytorch-tutorials.git
cd pytorch-tutorials
pip install -r requirements.txt

# Run Python scripts directly
python 01_pytorch_basics/pytorch_basics.py

# Or use Jupyter notebooks for interactive learning
jupyter notebook
# Then navigate to any tutorial folder and open the .ipynb file

1. PyTorch Basics

   • Tensors, operations, and computational graphs
   • NumPy integration
   • GPU acceleration
   • Basic autograd operations

2. Neural Networks Fundamentals

   • Linear layers, activation functions, loss functions, optimizers
   • Building your first neural network
   • Forward and backward propagation
   • nn.Module and nn.Sequential

3. Automatic Differentiation

   • Autograd mechanics
   • Computing gradients
   • Custom autograd functions
   • Higher-order derivatives

4. Training Neural Networks

   • Training loop implementation
   • Validation techniques
   • Hyperparameter tuning
   • Learning rate scheduling
   • Early stopping

5. Data Loading and Preprocessing

   • Dataset and DataLoader classes
   • Custom datasets
   • Data transformations and augmentation
   • Efficient data loading techniques
   • Batch processing

6. Convolutional Neural Networks
   • CNN architecture components
   • Convolution, pooling, and fully connected layers
   • Image classification with CNNs
   • Transfer learning with pre-trained models
   • Feature visualization

• Object detection (YOLO, R-CNN)
• Semantic segmentation
• Instance segmentation
• Image generation
• Style transfer

7. Recurrent Neural Networks

   • RNN architecture
   • LSTM and GRU implementations
   • Sequence modeling
   • Text classification
   • Text generation
   • Time series forecasting

8. Transformers and Attention Mechanisms

   • Self-attention and multi-head attention
   • Transformer architecture
   • BERT and GPT model implementations
   • Fine-tuning pre-trained transformers
   • Positional encoding

9. Generative Models

   • Autoencoders
   • Variational Autoencoders (VAEs)
   • Generative Adversarial Networks (GANs)
   • Diffusion models
   • Style transfer

10. Model Deployment

    • TorchScript and tracing
    • ONNX export
    • Quantization techniques
    • Mobile deployment (PyTorch Mobile)
    • Web deployment (ONNX.js)
    • Model serving

11. PyTorch Lightning

    • Lightning modules
    • Trainers and callbacks
    • Multi-GPU training
    • Experiment logging
    • Hyperparameter tuning with Lightning

12. Distributed Training

    • Data Parallel (DP) for single-machine multi-GPU
    • Distributed Data Parallel (DDP) for multi-node training
    • Model Parallel for large models
    • Pipeline Parallelism for deep networks
    • Fully Sharded Data Parallel (FSDP) for extreme scale

13. Custom Extensions

    • C++ extensions for custom operations
    • CUDA kernels for GPU acceleration
    • Custom autograd functions
    • JIT compilation with TorchScript
    • Binding C++/CUDA code to Python

14. Performance Optimization

    • Memory optimization techniques
    • Mixed precision training with AMP
    • Profiling and benchmarking
    • Data loading optimization
    • Gradient accumulation and checkpointing

15. Advanced Model Architectures

    • Graph Neural Networks (GNNs)
    • Vision Transformers (ViT)
    • EfficientNet and compound scaling
    • Neural ODEs
    • Capsule Networks

16. Reinforcement Learning

    • Deep Q-Networks (DQN)
    • Policy gradient methods (REINFORCE)
    • Actor-Critic and A2C
    • Proximal Policy Optimization (PPO)
    • Integration with OpenAI Gym

17. Model Optimization Techniques

    • Quantization (dynamic and static)
    • Pruning (structured and unstructured)
    • Knowledge distillation
    • Model compression
    • Hardware-aware optimization

18. Meta-Learning and Few-Shot Learning

    • Model-Agnostic Meta-Learning (MAML)
    • Prototypical Networks
    • Matching Networks
    • Reptile algorithm
    • Few-shot classification tasks

19. Neural Architecture Search

    • Random search and grid search
    • Evolutionary algorithms
    • Differentiable Architecture Search (DARTS)
    • Efficient Neural Architecture Search (ENAS)
    • Performance prediction

20. Bayesian Deep Learning

    • Bayesian Neural Networks
    • Variational inference
    • Monte Carlo Dropout
    • Deep ensembles
    • Uncertainty quantification

21. Advanced Research Topics

    • Self-supervised learning (SimCLR, BYOL)
    • Contrastive learning methods
    • Diffusion models
    • Neural Radiance Fields (NeRF)
    • Implicit neural representations

• 📖 README.md - Detailed theory and concepts
• 🐍 Python Script - Complete runnable code with comments
• 📓 Jupyter Notebook - Interactive step-by-step learning

• Python 3.8+
• PyTorch 2.0+
• torchvision
• torchaudio (for audio tutorials)
• matplotlib
• numpy
• pandas
• scikit-learn
• Jupyter Notebook/Lab

You can install the required packages using:

pip install -r requirements.txt

1. Sequential Learning: Follow the tutorials in order for a comprehensive learning experience
2. Topic-Based: Jump to specific topics based on your interests and needs
3. Practice: Each tutorial contains exercises and examples
4. Experiment: Modify the code and experiment with different parameters

1. Start with the README in each folder for theoretical background
2. Run the Python script to see the complete implementation
3. Open the Jupyter notebook for interactive learning and experimentation

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

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

• PyTorch team for the amazing framework
• The deep learning community for continuous innovation
• All contributors to this repository

Perfect for both beginners starting their PyTorch journey and experts looking to deepen their understanding of advanced topics!