deep-learning-roadmap

This repository is a curated collection of resources for learning and doing research in deep learning. It is not a library or a tool you install; it is an organized reference guide pointing to papers, courses, videos, frameworks, and datasets that are relevant to different areas of the field.

The structure is the main selling point. Rather than a flat list, the resources are organized into specific categories: types of neural network architectures (convolutional networks, recurrent networks, autoencoders, generative models), application areas (computer vision, natural language processing, speech, robotics), training techniques, and supporting topics like optimization and regularization. Each paper or resource entry includes a star rating indicating the curators' view of its importance, along with direct links to the paper and, where available, accompanying code.

Beyond papers, the collection covers online courses, YouTube lecture series, books, popular deep learning software frameworks, and benchmark datasets. The goal is to help someone who already knows they want to study a specific topic (say, recurrent networks or generative adversarial networks) quickly find the most relevant starting points without having to search across the internet.

The project is associated with a free Python machine learning book and a Slack group, both linked from the README. It accepts community contributions via pull requests, and there is a sponsorship link for supporting the maintainer.

The README is very long and covers a wide range of subtopics. It is written in reStructuredText format rather than standard Markdown, which is less common for GitHub READMEs. The file targets people who are already oriented toward deep learning as a field and need a structured map of where to go next. The full README is longer than what was shown.