This repository is an evolving, curated guide to learning Deep Learning, targeting individuals seeking a career in the field or a theoretical understanding. It provides a structured roadmap and links to free resources, aiming to alleviate choice overload for aspiring deep learning practitioners and researchers.

How It Works

The guide follows a phased approach, starting with prerequisites (Python, math fundamentals), progressing through core deep learning theory and practical application via MOOCs and coding exercises, and culminating in project-based learning and deeper dives into specialized areas like Computer Vision and NLP. It emphasizes a blend of theoretical understanding (e.g., deeplearning.ai) and hands-on implementation (e.g., fast.ai).

Quick Start & Requirements

• Installation: No direct installation required; it's a guide to external resources.
• Prerequisites: Python proficiency, foundational knowledge in Calculus, Linear Algebra, and Statistics are recommended but can be learned concurrently.
• Resources: Primarily free online courses (MOOCs), YouTube channels, blogs, books, and research papers. GPU access is recommended for practical exercises, with free options like Google Colaboratory and Kaggle Kernels suggested.
• Links: deeplearning.ai, fast.ai, Kaggle, Google Colaboratory.

Highlighted Details

• Comprehensive coverage from foundational math to advanced topics like GANs, Transformers, and Reinforcement Learning.
• Curated links to specific course lectures, assignments, and supplementary materials from top universities (Stanford, MIT, CMU, Oxford).
• Practical advice on project implementation, paper reading, and staying updated via blogs, podcasts, and Twitter.
• Includes resources for specialized areas like Self-Driving Cars and Meta-Learning.

Maintenance & Community

The guide is explicitly marked as "incomplete" and "in progress," indicating ongoing updates. It references prominent figures in the field like Andrew Ng, Jeremy Howard, and Andrej Karpathy. Community interaction points are implied through links to platforms like Kaggle and forums.

Licensing & Compatibility

The guide itself is a collection of links to external resources, each with its own licensing. The content referenced is generally free for educational purposes.

Limitations & Caveats

The guide's effectiveness relies heavily on the user's self-discipline and ability to navigate the vast array of linked resources. Some linked MOOCs may require paid specializations for full access to assignments. The rapidly evolving nature of deep learning means some linked resources might become outdated.