[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzg0Mjc","title","Practical TypeScript Artificial Intelligence Programming","state","published","contactAuthorMethod","email","aboutText","

This book is a practical, hands-on introduction to artificial intelligence for TypeScript and JavaScript programmers. It covers the full spectrum of modern AI — from classical machine learning algorithms implemented from scratch to deep learning with TensorFlow.js, large language model integration via cloud APIs and local models, symbolic AI techniques, and knowledge representation with graph databases and the Semantic Web. All examples run as command-line programs on Node.js with no browser or UI framework required.

The book is organized into five parts that can be read in any order. Part 1 covers the foundations of machine learning with TypeScript, including K-nearest neighbors, regression, clustering, exploratory data analysis, and anomaly detection — all built with nothing more than Node.js built-in modules. Part 2 dives into deep learning with TensorFlow.js, covering neural network fundamentals, natural language processing, image generation, reinforcement learning, and recommendation systems. Each chapter balances conceptual explanations with working TypeScript code.

Part 3 focuses on large language models, arguably the most transformative development in AI. You will learn the transformer architecture, then build practical applications using the Google Gemini and OpenAI APIs, run open-weights models locally with Ollama, and construct a production-quality AI command-line REPL with search grounding and persistent caching. Parts 4 and 5 round out the book with symbolic AI (frame-based knowledge representation, constraint satisfaction, and a complete chess engine with alpha-beta search) and knowledge representation (SPARQL queries against Wikidata, SQLite knowledge bases, RDF, and linked data).

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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