Hand-curated reading lists in the spirit of GitHub's awesome-* collections — the best courses, books, libraries, datasets, and papers per field, organized so you can go from zero to working knowledge without drowning in tabs. Each list is opinionated and kept tight.
Courses, books, frameworks, datasets, and the canonical papers across classification, detection, segmentation, generative, 3D, video, and vision-language.
reinforcement-learningCourses, books, libraries, environments, and the landmark papers across value-based, policy-gradient, model-based, offline, multi-agent, and RLHF.
generative-aiCourses, frameworks, serving (vLLM/Ollama), RAG + vector stores, fine-tuning, eval, agents (MCP/LangGraph), image/video/audio gen, and the landmark LLM papers.
roboticsCourses (Modern Robotics/Tedrake), ROS 2, simulators (Gazebo/MuJoCo/Isaac), perception/SLAM, planning/control, and the learned-robotics wave (LeRobot/RT-2/Diffusion Policy).
nlpCourses (CS224N), books (Jurafsky & Martin), libraries (Transformers/spaCy), datasets (GLUE/SQuAD/MMLU), and canonical papers from word2vec to instruction-tuned LLMs.