This repository provides open-source guides and code for mastering deep learning, from foundational concepts to production deployment, targeting aspiring and practicing engineers. It offers a structured, top-down learning approach with extensive Python and PyTorch examples, aiming to accelerate understanding and practical application.

How It Works

The project is structured around a website (www.deeplearningwizard.com) powered by MkDocs, hosting comprehensive tutorials. It covers a broad spectrum of topics including machine learning, deep learning (PyTorch, CNNs, RNNs, LSTMs), deep reinforcement learning, data engineering, and general programming. The approach emphasizes both theoretical understanding and practical coding, with a focus on efficient implementation using libraries like PyTorch, RAPIDS, and Numba.

Quick Start & Requirements

• Install: Primarily through cloning the repository and accessing the MkDocs-hosted website. Specific code examples may require pip install for libraries like PyTorch, NumPy, pandas, scikit-learn, RAPIDS, Ollama, LlamaIndex, and Huggingface.
• Prerequisites: Python, PyTorch, and potentially CUDA for GPU acceleration. Specific sections may require Apptainer for HPC environments.
• Resources: Setup time varies based on individual code examples; GPU resources are beneficial for deep learning tasks.
• Links: www.deeplearningwizard.com

Highlighted Details

• Comprehensive coverage from ML fundamentals to production deployment.
• Includes "from scratch" implementations for core deep learning concepts.
• Integrates GPU acceleration via RAPIDS (cuDF, cuML).
• Covers modern topics like LLMs, RAG, and MMLM.

Maintenance & Community

The project is maintained by Ritchie Ng, with contributions and support from academic and industry professionals (e.g., Jie Fu from MILA, Alfredo Canziani from NYU). Community interaction is encouraged via GitHub issues for bugs/improvements and pull requests for contributions. Links to YouTube, Twitter, Facebook, and LinkedIn are provided.

Licensing & Compatibility

The repository content is open-source. Specific licensing for code snippets or underlying libraries should be verified per component. Generally compatible with commercial use, but users should confirm licensing of individual dependencies.

Limitations & Caveats

The project is described as an "early work in progress," with gradual uploading of guides. Users should expect ongoing development and potential incompleteness in certain sections.