Books

The Local AI Stack: Building a Sovereign Machine Learning Workstation with Hyper-V, WSL2, and GPU Virtualization Renting GPU time from AWS costs more than the GPU. Every prompt you send to a cloud API is a prompt someone else logs. If your ML work runs on hardware you don't own, on a network you don't control, then you don't own your ML work.

The Quantization Black Book The definitive engineering reference for 4-bit quantization of large language models — from 70B all the way to 400B parameters. Written for the people who actually have to fold these models onto real hardware and keep them fast, accurate, and deployable. This is not an overview. It is a deep technical field manual covering the full quantization stack: the math, the algorithms, the hardware setup, the calibration process, the runtime kernels, and the memory tricks that make the difference between a model that runs and a model that doesn't.

📘 Advancing Technical AI via Curriculum Reasoning — 119‑Page Engineering BlueprintA research‑grade handbook for building high‑precision reasoning models through structured curriculum design.This 119‑page ebook is a complete, end‑to‑end framework for Machine Learning Engineers, AI Researchers, and systems architects who want to push LLMs beyond pattern matching and into true inferential reasoning.

AI is basically a very well-optimized math system pretending to sound like a person. That's it. That's the whole secret.This book explains exactly how, no math, no code, no jargon. Just a straight conversation about what's actually happening inside the machine everyone won't stop talking about.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.

Teaser TextExplore six advanced AI applications shaping the future.Build intelligent solutions for smart farming, AI-powered content creation, predictive healthcare, smart-city traffic optimization, conversational AI, and AI-generated music.From predicting crop yields and monitoring health to optimizing traffic and creating music with GANs, this volume demonstrates the practical possibilities of modern Artificial Intelligence.Six projects. Six domains. One journey toward advanced AI innovation.

Teaser TextBuild the future with advanced Artificial Intelligence.The final volume of the 50 AI Projects series presents 10 advanced projects covering AI code review, news bias detection, blockchain voting, deepfake detection, smart farming, AI content creation, predictive healthcare, smart-city traffic optimization, conversational AI, and AI-generated music.Explore emerging AI technologies while following the complete Software Development Life Cycle—from problem definition and system architecture to development, testing, deployment, and future enhancement.50 projects. 5 volumes. One complete journey into practical AI development.

Teaser TextEnter the world of advanced AI engineering.Volume 4 presents 10 advanced Artificial Intelligence projects covering medical diagnosis, crime prediction, autonomous drones, predictive maintenance, disaster management, conversational AI, financial fraud detection, legal document analysis, smart shopping, and autonomous vehicle vision.Go beyond basic models and explore how complex AI systems can be planned, designed, developed, tested, deployed, and enhanced using a complete Software Development Life Cycle.From intelligent models to intelligent systems.

Teaser TextTake your AI projects to the next level.Explore 10 intermediate-level Artificial Intelligence projects covering NLP, Machine Learning, Computer Vision, prediction, classification, sentiment analysis, image recognition, and voice-based applications.From fake news detection and student performance prediction to resume ranking, plant disease identification, sign language recognition, and language translation, Vol-2 takes you deeper into practical AI development through a complete Software Development Life Cycle.Learn the concepts. Build the systems. Think like an AI developer.

Teaser TextLearn AI by building real projects.Explore 10 practical beginner-level Artificial Intelligence projects and learn how to take an idea from problem definition to design, coding, testing, implementation, documentation, and future enhancement.From chatbots and face detection to resume screening, NLP, emotion detection, and intelligent recommendation systems, Vol-1 provides a structured introduction to practical AI development through the complete Software Development Life Cycle.Don’t just study AI. Build it.

Running LLMs locally is easy. Running them well is an engineering problem. Practical LLM Inference gets into the details that matter, from quantization and GGUF internals to GPU offloading, benchmarking and production deployment. Learn how to size hardware, find bottlenecks and build fast, reliable inference systems without the AI hand-waving.

Most vector search books start with the database. This one starts with the machine. Build a search engine from scratch, then push it from brute force to billion-scale retrieval with SIMD, HNSW, quantization and distributed systems. By the end, vector search won't be a black box. It'll be something you know how to build, tune and scale.

AI can write code in seconds, but can you trust what it creates? Securing AI-Generated Code shows you how to uncover hidden vulnerabilities, stop risky dependencies, secure AI coding agents and build safer development pipelines. A practical guide for teams that want the speed of AI without putting security on the line.

Building fast LLM systems takes more than a powerful GPU. This book shows you what actually happens between prompt and response, where latency hides and how to fix it. Work from kernels and inference engines to distributed clusters, benchmarking and production reliability, with practical guidance for building systems that stay fast under real-world load.

AI coding agents can move fast, but speed without clear intent creates expensive mistakes. This practical guide shows how Behavior-Driven Development turns specifications into a reliable contract between you and autonomous coding systems, helping agents build the right thing, catch problems earlier and produce software you can trust.