Books

Elegant Design Principles distils decades of design wisdom into 95 actionable principles spanning core OO, SOLID/GRASP, package design, reliability and a forward‑looking AI‑first approach. Explore the Design Pyramid to understand how quality attributes, smells and principles interconnect; learn to manage complexity through high cohesion, low coupling and clear abstractions; and adopt modern practices like test‑driven development and semantic modularity. From novices seeking a roadmap to experts embracing AI‑assisted workflows, this book equips you to create systems that are robust, maintainable and elegant—today and in the AI‑driven future.

? What’s New in Release 14? Unlock the full potential of AI with the Generative AI Professional Prompt Engineering Guide—your ultimate resource for mastering prompt engineering and generative AI. Whether you're a beginner or an expert, this guide is more than just a book. It’s an evolving learning platform packed with the latest resources and tools to help you stay at the forefront of AI innovation.This AI‑driven learning platform uses AI tools to make it much more than a book.  ✅ Revision and Expansion of Chapter 4: Advanced AI Prompting Techniques Book Companion GPT, 50+ hours of additional learning from 6 different companion podcasts and Podcast reviews Once you purchase it you will receive every future release of the book! New release ~ every month.

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

A practical field guide for using AI assistants at work—and engineering them in production. Learn the standards that prevent “sounds right, wrong” outputs: specs, grounding, tools, evals, guardrails, and cost control.

Code isn’t just for humans anymore. Agent-Friendly Code is your guide to building clean, modular, and AI-native systems that collaborate with tools like Cursor, Copilot, and GPTs. Learn how to design smarter codebases with flows, metadata, and naming patterns that empower both developers and intelligent agents. The future of development starts here.

I've always wanted to write a technical book. My interest in AI and Spring led me to explore, take notes, and contribute to the Spring AI project. This journey naturally progressed into sharing my knowledge through a book as my notes expanded. Spring AI for Your Organization offers a clear guide for integrating Spring AI with Google Vertex AI. It's ideal for both seasoned backend developers and newcomers, aiming to enhance your skills and understanding of AI in development. The book covers a range of topics from chatbots to advanced AI features, providing step-by-step instructions for using GCP Vertex AI. Dive into this comprehensive guide and start building smarter Spring applications today.

"It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure.

Unlock the black box of Deep Learning. This book takes you on a journey from the humble dot product to the elegant complexity of Backpropagation via Matrix Calculus. No magic, just math and code. Perfect for developers who want to understand the 'why' and 'how' behind the equations, derived step-by-step from first principles.

Imagine a group of LEGO superheroes facing missions that require planning in layers. One hero scouts the terrain. Another maps the risks. A third builds the route. All these steps connect. None of them works alone. This is Nested Learning in action.Google uses this layered thinking to help AI break problems into parts, solve those parts in sequence, and combine them into stronger results

AI is reshaping industries and products. This book helps Product Managers understand how to use AI tools to plan, design, and deliver great products. It explains the AI stack and strategic frameworks for integrating AI features into products; how to use GenAI to perform customer discovery, market research, and prototypings; and how the Product Development Life Cycle and the Model Development Life Cycle intersect when creating AI-driven products.

Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?" The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly. This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.

Want to supercharge your ML and AI work? Learn from algorithmic traders' best practices.

In diesem Primer führen wir systematisch in die Konzepte und Architektur von RAG ein. Wir behandeln sowohl theoretische Grundlagen als auch praktische Implementierungsaspekte wie Chunking, Embedding und Vektordatenbanken. Außerdem teilen wir unsere Praxiserfahrung aus echten Projekten. Für Softwarearchitekt:innen und -entwickler:innen, die einen kompakten, aber fundierten Einstieg ins Thema suchen und den Einsatz von RAG in der eigenen Organisation bewerten wollen.