This repository provides a comprehensive collection of PyTorch tutorials, ranging from fundamental concepts to advanced applications in computer vision, NLP, and generative models. It is designed for both beginners seeking hands-on experience and experts looking to explore cutting-edge techniques, offering practical, runnable code examples and detailed explanations.

How It Works

The repository is structured into thematic sections, covering PyTorch basics, neural network fundamentals, data handling, and advanced topics like generative models, transformers, and distributed training. Each tutorial includes a detailed README for theory, a runnable Python script for implementation, and an interactive Jupyter notebook for step-by-step learning and experimentation.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.8+, PyTorch 2.0+, torchvision, torchaudio, matplotlib, numpy, pandas, scikit-learn, Jupyter Notebook/Lab.
• Usage: Run Python scripts directly (e.g., python 01_pytorch_basics/pytorch_basics.py) or use Jupyter notebooks for interactive learning.

Highlighted Details

• Covers a wide spectrum of PyTorch applications, from basic tensor operations to advanced topics like Neural Radiance Fields (NeRF) and self-supervised learning.
• Each tutorial is self-contained with theory (README), code (Python script), and interactive notebooks (.ipynb).
• Includes sections on model deployment (TorchScript, ONNX), PyTorch Lightning, and distributed training strategies (DP, DDP, FSDP).
• Features advanced topics such as Graph Neural Networks (GNNs), Vision Transformers (ViT), and meta-learning.

Maintenance & Community

The repository welcomes contributions via Pull Requests and encourages discussion of major changes through issues.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The repository requires PyTorch 2.0+, which may be a breaking change for users on older versions. Some advanced topics might assume prior knowledge of deep learning concepts.