Books

Why learn one programming language when you can understand three at once? This book introduces the foundations of programming through a powerful side-by-side comparison of Python, Java, and C#, enabling readers to build strong conceptual understanding, transferable coding skills, and a solid pathway toward modern software engineering.

Your first AI integration is usually simple: one provider, one SDK, one model. Then production happens. Costs rise. Rate limits appear. Teams need access control. Security wants API keys centralized. Product wants to test better models. Finance wants to know who is spending what. Suddenly, the real problem is not calling an LLM. The real problem is operating AI as shared infrastructure. The End of AI Vendor Lock-In shows how LiteLLM helps teams build provider-agnostic, resilient, cost-aware AI systems that can route across models, manage fallbacks, track spend, enforce budgets, and reduce dependency on any single provider. If you are building AI products that need to survive real-world production pressure, this book is for you.

PYTHON COMPLETE GUIDE is a comprehensive, hands-on guide to learning Python from the ground up. Covering everything from core concepts lto advanced topics. This book empowers readers to develop real-world skills.

Build a city. Not a toy project — a living system.In Masters of the Metropolis, you don’t learn programming by memorising syntax. You learn by constructing a simulation that evolves over time.Every chapter adds something real: time, citizens, economy, infrastructure, events.By the end, you’ve built more than code. You’ve built a system that behaves.

This book is specially written for ML engineers who know what a groupby is but want to know why it's slow and how to fix it; data scientists who understand sentiment analysis but want to see how it connects cleanly to a Pandas pipeline; and data engineers who ship Pandas code to production and need to know which patterns will break on Pandas 3.0 and which are safe.

Master the engineering principles behind Unsloth to fine-tune Large Language Models on limited hardware. Discover technical workflows for 4-bit quantization, custom Triton kernels, and Direct Preference Optimization. This guide covers the full lifecycle, including context expansion and production deployment via GGUF and vLLM. A rigorous reference for developers seeking to maximize LLM performance on consumer-grade GPUs.

Python Engineering Judgment is for engineers who already know Python and want to build the skill AI makes more valuable: the ability to reason about code, systems, tradeoffs, and failure modes. This deep, practical field guide teaches the Python object model, type system, decorators, generators, concurrency, asyncio, multiprocessing, API design, packaging, performance, debugging, security, observability, and reliability through the lens of real-world engineering judgment. Use it to prepare for senior, staff, principal, and tech-lead interviews, and to make better decisions in code reviews and production systems.

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.

Working with PySide6 should not depend on trial and error. This book replaces fragmented learning with a structured approach, combining in-depth explanations and dependable code examples that move you forward with confidence.

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。

Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.