Resource index

To begin doing something, follow the learning routes. To find a particular resource, start here. Courses, articles, and projects are grouped by use so you can try another explanation, go deeper, or find an author's original.

Courses and papers

• Courses, textbooks, and foundations: computing, Python, mathematics, ML/DL, and architectures, including Crash Course, 3Blue1Brown, Stanford, and MIT.
• Mu Li: courses and paper readings: connecting D2L and close reading to actual learning.
• Courses, papers, and projects by direction: vision, LLMs, multimodal models, generation, RL, world models, embodied AI, agents, systems, AI4X, graphs, and time series. Includes Lumina, awesome lists, and paper lists.

Doing research: methods and process

• Research methods and tools: understanding research, asking questions, reading, experiments, writing, talks, AI tools, and reference management.
• Author blogs and research notes: Yuwei Niu, Wenhao Chai, Jiaxuan Zou, Xiaohan Ding, Xiaoguang Han, and Jianlin Su, with specific articles and questions.
• Research experience, Q&A, and AMAs: Han's original post on good research, contacting faculty, research judgment, Reddit questions, and AMAs.

Growth and a wider view

• LessWrong selections: when advice applies, growth, experimental judgment, choices, and AI use. Choose from a question of your own.
• Research groups and teams: what to look for, how to follow papers to people, and what to check before contact.
• Blogs, interviews, and information channels: authors, media, original releases, and perspective essays. See Lookout for following research.
• BlogrXiv · OpenEnvision: AI research blogs, lab essays, and technical notes organized by field, with links to the original writing.
• ScholarTube · OpenEnvision: Researcher interviews, video podcasts, complete courses, and research talks, searchable by field and linked to the original videos.