Books

Most books about ChatGPT explain the magic. This one shows you the math. Inside Large Language Models, Volume I takes a curious beginner from "what is an LLM" to a complete, trained GPT, with nothing more than high-school algebra, a working laptop, and a willingness to read carefully. Every formula is walked through by hand. Every line of code comes with a plain-English explanation. By the end you will have built, trained, and run your own transformer from scratch, and you will know exactly what is happening inside. No PhD or Data Science required. No prior machine learning needed. Just curiosity and a calculator.

Most books about ChatGPT explain the magic. This one shows you the math. Inside Large Language Models, Volume I takes a curious beginner from "what is an LLM" to a complete, trained GPT, with nothing more than high-school algebra, a working laptop, and a willingness to read carefully. Every formula is walked through by hand. Every line of code comes with a plain-English explanation. By the end you will have built, trained, and run your own transformer from scratch, and you will know exactly what is happening inside. No PhD or Data Science required. No prior machine learning needed. Just curiosity and a calculator.

跳过黑箱框架。用纯 Python 从零构建生产级 AI 编程智能体——云端或本地，用 pytest 测试，全部在一个文件中完成。

Olvídate de los frameworks de caja negra. Construye un agente de programación de IA de nivel profesional desde cero en Python puro — en la nube o local, probado con pytest, todo en un solo archivo.

ブラックボックスのフレームワークは不要。純粋なPythonでプロダクションレベルのAIコーディングエージェントをゼロから構築。クラウドでもローカルでも、pytestでテスト済み、すべて1つのファイルに収まります。

Keyword search misses meaning. Vector search misses precision. This book shows you how to combine them into production systems that deliver both, with architecture patterns, model selection frameworks, evaluation methodology, and operational guidance grounded in primary research.

What if time is not merely a clock, but a filter? What if systems fail not because they lack information, but because correction arrives too late to matter? What if technologies become dominant not because they are best, but because they have already reorganized the future around themselves? And what if Human–LLM collaboration is no longer just a sequence of prompts and answers, but an evolving ecology shaped by its own artifacts? Force–Information–Time: Essays on Structural Evolution is a companion volume to the FIT framework. Rather than restating the core theory, it explores what becomes visible when structural thinking is carried across learning, institutions, science, technology, Human–LLM collaboration, and human life.

Build your first on-device ML feature using mental models and engineer intuition, not math. A practical guide for Android developers — from Python training to TFLite inference in production.                               

Gradient-boosted trees have dominated tabular ML for a decade. A new class of pretrained models just broke through — making accurate predictions on unseen datasets in seconds, with zero gradient steps on your data. This book explains why it works, when it fails, and how to deploy it.

If a humanoid robot carries about $28,000 a year in fixed cost, then at $60 contribution per billable hour it breaks even at roughly 467 billable hours per year, or just under 9 hours per week. But if supervision is heavier and contribution drops to $28 per hour, break-even jumps to 1,000 hours per year, or about 19.2 hours per week.

Artificial Intelligence has become the new global power race. Nations are competing for data, infrastructure, and algorithmic dominance that will define the future world order.

Unlock the full potential of C++ to revolutionize your AI and machine learning projects with this definitive guide to high-performance computing. Whether you're an experienced developer or an ambitious newcomer, this book is your gateway to mastering the art of optimizing computational efficiency for cutting-edge AI solutions.

The game theory already inside your ML toolkit, explained for working engineers. Covers GANs, SHAP, auctions, strategic classification, MARL, federated learning, and LLM alignment.

AI made creation cheaper. It also made judgment more valuable.Taste: Turning Vibe into Assets in the AI Age shows why the next wave of winners will not simply be the people with the best credentials or the biggest teams, but the ones who can turn instinct, clarity, and initiative into real assets.