Books

Linear Programming and AI Optimization Models (VOL-3) delivers practical mastery in scheduling, supply chain, logistics, hybrid AI-OR systems, large-scale cloud optimization, and emerging technologies. With Python implementations, industry case studies, and forward-looking research trends, this volume turns theory into powerful real-world solutions.

Take your optimization skills to the next level with AI.Linear Programming and AI Optimization Models (VOL-2) dives deep into the algorithms driving modern Machine Learning and Intelligent Systems. Master Gradient Descent, Adam, Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization, Constraint Satisfaction, and advanced Network Models.

Unlock the power of optimization in the age of AI."Linear Programming and AI Optimization Models" delivers a masterful blend of classical Operations Research and modern Machine Learning applications. From the elegant Simplex Method to advanced decomposition techniques and KKT conditions, this Volume-1 builds a rock-solid foundation while demonstrating how these powerful algorithms .

OpenClaw in Production shows you how to run OpenClaw as a secure, reliable service that can handle real workloads. Whether you're deploying on a Raspberry Pi or operating a Kubernetes cluster, you'll learn the practical skills needed to keep your agents stable, secure, and easy to manage as they grow from a single instance to production at scale.

Enterprise Retrieval-Augmented Generation with C# is a practical guide to building production-ready AI applications in the modern .NET ecosystem. Learn how to design scalable, secure, and high-performance RAG systems through real-world C# examples, proven architectures and enterprise best practices.

Large language models are not the architecture. Enterprise Intelligence Architecture shows how memory, governance, orchestration, validation, and model routing work together to build AI systems that are reliable, explainable, and production-ready. A practical blueprint for architects, AI engineers, and technical leaders.

This practical guide shows you how to build production-ready speech recognition applications from the ground up. Learn how to use Voice Activity Detection, choose the right ASR models, build real-time inference pipelines, and deploy scalable systems with hands-on examples and modern open-source tools.

Build real-world AI applications with Python—not just demos. Learn LLM APIs, RAG, embeddings, vector search, AI agents, prompt engineering, security, deployment, and production-ready architectures through practical projects and expert guidance. Gain the skills to build, ship, and scale modern AI systems with confidence.

Master DeepSeek V3 — one of the most capable AI models of its time.This practical guide teaches students, researchers, and professionals how to use DeepSeek V3 for learning, research, coding, content creation, automation, and productivity — with strong focus on prompt engineering and responsible AI usage.

Unlock the power of Llama 4 — the next generation of open-weight multimodal AI.This practical guide shows students, researchers, and professionals how to harness advanced AI tools for learning, research, teaching, and productivity. From generating study notes and research ideas to building educational chatbots and optimizing workflows, discover how Llama 4 can transform the way you work and learn

A visual journey through the world of Large Language Models. Learn how modern AI systems work using diagrams, mental models and simplified explanations—without getting lost in unnecessary complexity

AI application development is moving fast, but building systems that actually work in production is still difficult. This guide shows you how to build, deploy and scale AI applications with Dify, covering visual workflows, RAG, AI agents, integrations, security and production operations. It's a practical, hands-on resource for turning prototypes into reliable real-world applications.

From Prompt to Autonomous Agent is a practitioner’s guide to building AI systems that actually run in production – written for engineers who build things that need to work at 3am, six months after launch.The book teaches twelve levels of agentic AI architecture using a single, unbroken running example: a job failure in AWS Glue that requires a root cause diagnosis. This same system evolves over the fifteen chapters - from a single API call in Chapter 1 to a Society of Agents deliberating over a novel multi-factor failure in Chapter 12. You don’t just know what each pattern is by the end. You know what it is for, how much it costs, and what it gives you over the level below.Each chapter focuses on a single pattern: the Basic Responder, the Router, Tool Calling, Memory Augmentation, the Autonomous Agent, Reflective Self-Critique, Human in the Loop, Planning, Multi-Agent Coordination, Tool Building, Hierarchical Orchestration, and the Society of Agents. Each chapter has working code (one file, under 100 lines, no dependencies other than the Anthropic SDK) and a Failure Museum - a catalogue of real production failures with the signal, the cause, and the fix.The final section, Production and Craft, covers what most AI books entirely skip over, how to make agentic systems observable, how to protect them with cost guardrails, how to build fallback chains that degrade gracefully, and how to evaluate them with enough discipline that you know they're working before your users tell you they aren't.The book is for data engineers, backend engineers, and ML engineers who are done with demos and ready to ship. Requires Python fluency and engineering judgement. No prior knowledge of AI research, math or language models is assumed.The examples are in Python and use the Anthropic SDK because that’s where real systems live. They are based on real AWS infrastructure , EventBridge, Glue, Lambda, Bedrock, Snowflake, OpenSearch.The core principle runs through all the chapters: the right level is always the lowest level that can reliably handle your problem. This book will show you how to figure out what level that is.

The latest version of Rust (1.85) has some great new features, like async closures, more stable associated function return types, and const generics that are now mature enough to underpin serious numerical libraries. The linfa and smartcore ecosystems have developed into decent classical machine learning stacks. The Burn training framework feels native to Rust, not like it's been ported from it. The Candle makes it so that loading pre-trained transformer models is more of an engineering task than a research exercise. The crates that used to need all sorts of workarounds now just work.

When something trips and two hundred alarms fire at once, which one is the cause? From Flood to Cause shows you how to build deterministic, grounded, auditable root-cause analysis where a causal engine decides and a language model only explains — never diagnoses, never guesses, and abstains the moment grounding is absent. Every concept is wired to a live, zero-install NEXUS-1 console in your browser, so you don't just read the architecture — you drive it.