Books

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!

This book familiarizes readers with the world of LLM and agentic AI, and helps them quickly gain a working-level knowledge of building useful agentic AI solutions for process industry operations. With no prerequisites required, practical demo applications, and a hands-on approach adopted throughout, this book makes advanced AI technologies accessible to process engineers and data scientists alike. It aims to help process data scientists and engineers take their first confident steps into Agentic AI world, understand the full picture, and build a strong enough foundation to keep learning and building on their own. Also available here.

Data Mesh is a sociotechnical approach that enables development teams to autonomously carry out data analysis. In this primer, we explain Data Mesh from the engineering perspective.

In diesem Primer erklären wir Data Mesh aus der Engineering-Perspektive.

Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

Are you an above-average Python coder? Prove it! Coffee Break Python - Mastery Workout helps you boost your Python skills and reach mastery level. The approach is simple: you solve 99 really hard Python puzzles that get harder as you progress with the book. A clear path to Python mastery!

Reading "Learn Python the Simple Way", you'll learn how software work.At this moment, Python is one of the fundamental programming languages for Machine Learning (ML), Scientific and Numeric Computing, Robotics and even Web Development.

A local model returns the expected answer. How do we know which layers ran on the GPU? In Local AI on RTX Spark Laptops, we investigate questions like this through 251 hands-on WSL2 exercises, following the evidence from model-loader logs down to individual CUDA kernels.

A research monograph connecting quantum annealing, Aizenberg MVN theory and complex spectral surrogate models, with detailed applications to battery energy storage placement in power grids.

What really happens inside a neural network? This book explores the tools and ideas researchers use to uncover how AI systems work, from foundational concepts to practical experiments. Learn to separate solid findings from bold claims, develop useful research skills and prepare for a field that is changing as quickly as the technology itself.

From your first predictive model to the ensembles that win competitions — the machine-learning core of the Data Science, End to End series, built from first principles in clean, idiomatic Python.

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.

Decision models answer a typed question with a calibrated probability in one forward pass: no token stream, no parsing, no values outside your schema. This guide explains TypeSafe's Jev and the open "JEV-like" family, then shows how to choose, run, serve, fine-tune and evaluate local variants on your own hardware.

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.

Build the engineering foundations required to design and develop production-grade AI systems—from Python and FastAPI to databases, security, semantic search, RAG, LLM architecture, and AI application performance.Go beyond Python programming and start thinking like an AI Engineer.The AI Engineer — Volume I takes you from professional Python engineering to the foundations of modern AI application development.You will build the engineering knowledge required to create reliable AI systems—not just experimental notebooks or simple API calls.A project-based AI engineering book covering Python production engineering, FastAPI, PostgreSQL, authentication, testing, reliability, embeddings, semantic search, RAG, LLM architecture, AI pipelines, caching, and performance optimization.AI EngineeringAI EngineerArtificial IntelligenceGenerative AILLMLarge Language ModelsPythonFastAPIRAGRetrieval Augmented GenerationSemantic SearchEmbeddingsVector SearchLLM ApplicationsAI SystemsMachine Learning EngineeringBackend EngineeringProduction AIAI ArchitectureSoftware Engineering