Learn AI by Building
Free tutorials, deep-dive series, and hands-on Jupyter notebooks for AI engineers and data scientists.
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View All →LLM Agent Cookbook
Build AI agents from scratch — ReAct, Tool Use, Multi-Agent orchestration
ML Cookbook
Master machine learning algorithms with hands-on Jupyter projects
Data Analysis Cookbook
SQL, Pandas, Statistics — everything for data-driven decisions
Ontology & KG Cookbook
RDF, OWL, Neo4j, and GraphRAG for knowledge-powered AI
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Starter Kits
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PremiumBuild Your Own autoresearch — Applying Autonomous Experimentation to Any Domain
Apply the autoresearch pattern to text classification, image classification, and RAG pipelines. Includes a universal experiment runner and program.md template.
PremiumRunning autoresearch Hands-On — Overnight Experiments on a Single GPU
From environment setup to agent execution and overnight results analysis. Tuning guide for smaller GPUs and practical tips.
Inside Karpathy's autoresearch — Building an AI Research Lab in 630 Lines
A code-level deep dive into Karpathy's autoresearch. Dissecting train.py, BPE tokenizer, MuonAdamW optimizer, and the agent protocol design.
PremiumAgentic RAG Pipeline — Multi-step Retrieval in Production
Build a full Plan-Retrieve-Evaluate-Synthesize pipeline. Unify vector search, web search, and SQL as agent tools. Add hallucination detection and source grounding.
PremiumSelf-RAG and Corrective RAG — The Agent Evaluates Its Own Retrieval
Implement Self-RAG reflection tokens and CRAG quality-based fallback. Build retry/fallback logic with LangGraph conditional edges.
Why Agentic RAG? — Query Routing and Adaptive Retrieval
Diagnose naive RAG limitations, classify query intent, and route to the optimal retrieval source with LangGraph. Implement adaptive retrieval that skips unnecessary searches.