Deep Learning Fundamentals: Code Materials and Exercises

This repository provides code materials and exercises for a Deep Learning Fundamentals course, targeting individuals seeking to learn and practice deep learning using PyTorch and PyTorch Lightning. It offers a structured curriculum from foundational machine learning concepts to advanced topics like computer vision and natural language processing, enabling users to build and train various neural network models effectively.

How It Works

The project delivers a comprehensive curriculum divided into units, each featuring accompanying code and practical exercises. It utilizes PyTorch for core tensor operations and neural network implementation, guiding users through concepts from basic ML workflows to advanced architectures such as CNNs, RNNs, Transformers, and LLMs. A key aspect is the integration of PyTorch Lightning, a framework that streamlines PyTorch code organization, training loops, and enhances reproducibility and scalability.

Quick Start & Requirements

• Prerequisites: Python, PyTorch. The README mentions setting up a computing environment and utilizing GPUs for accelerated training, suggesting GPU support is highly recommended.
• Links:
  • Course website: https://lightning.ai/pages/courses/deep-learning-fundamentals/
  • Discussion forum: https://github.com/Lightning-AI/dl-fundamentals/discussions

Highlighted Details

• Covers a broad spectrum of deep learning, from foundational ML workflows and PyTorch basics to advanced computer vision and NLP topics including Transformers and LLMs.
• Emphasizes practical application through hands-on exercises for each unit, enabling implementation of concepts like perceptrons, logistic regression, MLPs, CNNs, and RNNs.
• Integrates PyTorch Lightning for code organization, reproducibility, and efficient training, demonstrating best practices for scaling deep learning projects.
• Includes modules on optimizing training speed via mixed-precision, multi-GPU strategies, and hyperparameter tuning.

Maintenance & Community

• Developed in collaboration with Sebastian Raschka and Lightning AI.
• Community engagement is encouraged via the GitHub discussions forum.
• Updates and announcements are shared on Twitter via @LightningAI and @rasbt.

Licensing & Compatibility

• License information is not explicitly provided in the README.

Limitations & Caveats

• The README does not specify any limitations, alpha status, or known bugs. Effective utilization may require a robust computing environment, particularly for GPU-accelerated exercises.