Artificial Intelligence

Mathematical Foundations of AI and Data Science: Discrete Structures, Graphs, Logic, and Combinatorics in Practice transforms abstract mathematical concepts into practical tools for computational problem-solving.Explore logic, set theory, relations, functions, combinatorics, discrete probability, graph algorithms, trees, algebraic structures, Boolean systems, recurrence relations, optimization.

Most enterprise AI programs do not fail because the model is weak. They fail because the organization cannot operate AI: costs are hidden, ownership is vague, governance is late, vendors create dependency, and production risk is not clearly assigned. This book helps leaders fix the system around the model.

The AI Advantage shows you how to use artificial intelligence to work smarter, create more, save time, boost productivity, and discover new opportunities—even if you’re completely new to AI. With practical tools, strategies, examples, and prompts, you’ll learn how to turn AI into a powerful advantage for your personal and professional growth.

AI agents are only as good as the loops behind them. This book shows you how to build systems that plan, act, evaluate and improve with every step. Learn the patterns, frameworks and engineering practices behind reliable, self-correcting agents that solve real problems in production.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon

Learn Next.js by building a real AI-powered application, from project setup to production deployment. This book covers the complete journey: TypeScript, React, Next.js App Router, Prisma, authentication, the Vercel AI SDK (chat, RAG, tool calling), testing, security, and deployment. One project, 28 chapters, no toy demos.

Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU. In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers. Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either  InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible.   This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

Bridge the gap between high-level C# and raw hardware execution. Eliminate GC pauses with zero-allocation Span<T> and SIMD vectorization. Learn to build blazing-fast tokenizers and local LLM inference engines. Stop waiting for the runtime—start engineering for extreme AI performance.

Top-k is not relevance, retrieved text is not evidence and an LLM judging another LLM is not verification. Beyond “Chunk and Pray” shows how to build RAG that answers through a verified knowledge graph, preserves exact numbers, cites its sources and abstains when it cannot prove the answer.

The Codex Playbook helps enterprise engineering teams turn Codex from a helpful assistant into a reliable part of the software delivery system, with practical patterns for context engineering, repositories, GitHub workflows, MCP, governance, and AI-assisted development.

The first book that addresses engineering management specifically for the AI-native era. Classic EM books like The Manager's Path and An Elegant Puzzle predate agentic coding tools — this one starts where they left off. Every chapter produces a concrete artifact: a hiring rubric, a performance calibration guide, a budget template, a rollout plan. By the end, you have a complete management playbook for leading engineering teams where agents write most of the code.

Using AI to code is easy. Using it without losing control — not so much.By week three, your project stops moving. The agent forgets decisions it made ten days ago. A change in auth breaks the dashboard. You spend more time re-explaining context than writing features. The code works, but only you know why — and you're not even sure you remember all of it.That's vibe coding hitting its ceiling.Spec-Driven Development is the method that replaces the chaos with a spec the AI actually executes. Not a ceremonial document. A working artifact: PRD, issues, tests, code — all traceable, all connected, all in the right order.This book shows you:How to grill your own idea before writing a single promptHow to write a PRD the AI won't misinterpretThe 7 phases that turn an idea into working softwareHow to use GitHub SpecKit and openSpec (and when not to)How to work this way in a team without slowing downThe 5 anti-patterns that destroy every spec22,000 words. 13 chapters. 5 appendices with ready-to-copy templates.No theory dumps. No filler. Just the method.