Learning

It exists to teach developers how to build generative AI applications through 21 modular lessons that mix conceptual explainers with working Python and TypeScript code.

A structured 12-week curriculum that teaches symbolic AI, neural nets, and even genetic algorithms—without pretending deep math or cloud ML don't exist.

It teaches how LLMs work by implementing tokenization, attention, pretraining, and finetuning in pure PyTorch, one notebook at a time.

Hello-Agents is a free, 16-chapter curriculum that teaches developers to construct truly AI-driven agents from first principles rather than wiring no-code workflows.

To teach agentic design patterns through videos, notebooks, and documentation auto-translated into more than 50 languages.

Because skimming the abstract is not the same as understanding the architecture decisions.

This repo exists to sort the scattered open-source AI agent ecosystem into an industry-by-industry directory, saving you from GitHub search purgatory.

A free, end-to-end ML systems curriculum that treats the repository itself as the course — textbook, build-your-own framework, hardware kits, simulator, and interview prep included.

This repo exists because the LLM learning curve is a scattered mess of blog posts, so it organizes the field into three distinct tracks—fundamentals, scientist, and engineer—each paired with runnable Colab notebooks.

It corrals over 90 free courses, paper roundups, interview prep, and roadmaps into one sprawling LLM curriculum.

A free, 26-lesson curriculum marches beginners through classic machine learning the old-fashioned way: with homework, quizzes, and Scikit-learn.

It collects copy-paste role-play prompts that cast ChatGPT as a Linux terminal, proofreader, or interviewer for Chinese-speaking users.

Most ML tutorials end at training; this course walks you through the engineering required to actually ship it.

A curated index of OpenClaw skills, minus the spam, duplicates, and malware.

A maintainer cataloged every Chinese NLP repo they touched into a single, obsessively categorized list so others wouldn’t have to hunt.

Why rebuild the same RAG pipeline or agent loop from scratch when you can fork a working template instead?

A curated directory of machine learning frameworks and libraries across dozens of languages, maintained by a human gatekeeper tired of LLM spam.

A curated awesome-list that tries to answer "What is Data Science, and what should I study?" by cataloging courses, tools, libraries, and communities in a single sprawling index.

It collects 280+ importable n8n workflows so you can automate Gmail, Slack, or OpenAI pipelines without dragging nodes around.

To teach developers agent engineering from scratch using smolagents, LangGraph, and LlamaIndex, capped by an automated benchmark.