Books

Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.

Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.

AI engineering is becoming one of the most valuable and in-demand areas of software development, and Python, LangChain, and LangGraph are core skills for building the systems behind it. Learn how to turn basic Python knowledge into production-grade backend and agentic AI applications—and move toward an engineering niche centered on building and controlling AI rather than competing with it.

Designing AI is only half the journey. Learn how to deploy, secure, govern, monitor, and scale Enterprise AI systems for production with practical architectures and proven operational strategies.

You have built an AI agent that works beautifully in a demo, then falls apart the moment real users arrive: it invents answers, burns through tokens, and freezes on slow tools. The problem was never your prompt. This book shows you the real fix, layer by layer, and turns "I hope my agent works" into "I can prove it does."

Practical, safe AI for people who actually run networks.

A tutorial teaches you async, await, and an event loop. It does not teach you what happens when a supplier stalls, a queue fills, a client disconnects mid-request, or a deploy cancels a thousand in-flight tasks while cleanup still has to run.This is a short, working book about those moments. It targets Python 3.11+, and every technique in it is demonstrated by a program you can run and a test you can read.

A practical, code-first guide to physics-informed machine learning for scientists and engineers. You'll build a working Physics-Informed Neural Network from scratch in PyTorch, solve a real differential equation, and verify it against the exact solution — staying honest about both the power and the limits of the method.

The ultimate technical blueprint for developers and AI engineers to strip away execution overhead, leverage hardware-accelerated NumPy/PyTorch operations, and scale Python code effortlessly

Vague prompts produce vague results. This book shows developers how to write clear, testable prompts for real coding tasks—explanations, functions, debugging, tests, and more—using a simple, repeatable method: state the goal, give the right context, set constraints, define the output, and verify the result.Written for beginners and working developers who want better AI-assisted work without treating model output as automatically correct.

A practical 30-day plan that turns complete beginners into confident, responsible users of AI coding assistants.Learn how to set clear goals, build a small project, debug with purpose, use Git, write better code, and present your work — all while using AI as a learning partner, not a crutch.

AI can speed up learning and coding — but only if you stay in control. This book shows new developers how to use GPT-6, Claude, and Cursor effectively: how to ask clear questions, supply useful context, review generated code, test edge cases, and avoid common mistakes such as invented APIs, insecure suggestions, and overconfident explanations. You will build practical habits for explanations, small features, tests, debugging, and documentation — while protecting private data and remaining responsible for the final result. Ideal for students, self-learners, and early-career developers who want to learn with AI, not instead of learning.

Your LLM pipeline passed every check, and a fallback model still served 50 of its 71 successful calls. This case study follows one real multi-stage pipeline through its failures and shows how to make each stage traceable, attributable and honestly measured.

Your Sharpe ratio is 2.8. Your model is wrong.Not because the code is broken, but because somewhere between the raw data and the equity curve, the future leaked into the past. A centered rolling window. A normalization fit on the full sample. A fundamentals join on the wrong date. Each one runs clean, and each one produces a number that disappears the week real money replaces history.Machine Learning for Market Prediction is a quant reviewer’s playbook for building a pipeline you can actually trust: point in time data, leakage-free features, triple barrier labels, embargoed walk-forward validation, realistic costs, and SHAP audits that trace a suspicious feature to its root cause.It will not make your model more profitable. It will tell you the truth about it.Distrust your backtest. Then prove it right.

Your AI application works on your machine. What happens when it reaches production? Learn to deploy, monitor, secure, and scale AI systems using Docker, CI/CD, Kubernetes, cloud infrastructure, and practical production engineering. Move beyond prototypes and build AI applications you can operate with confidence.