Machine Learning

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.

Everything you really need to know in Machine Learning in a hundred pages.

Build a complete LLM inference engine in C++ — from a blank project to a working Transformer that loads a real model and generates text. Forged one challenge at a time, with tests that prove every piece works before you move on.

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.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

Helping C# and .NET developers to learn how to do machine learning and become highly sought-after (and well-paid) AI engineers. No prior experience of ML required!

Olvídate de los frameworks de caja negra. Construye un agente de programación de IA de nivel profesional desde cero en Python puro — en la nube o local, probado con pytest, todo en un solo archivo.

Bridge AI and science with this hands-on guide. Whether you're a researcher learning ML or an engineer entering scientific applications, build real systems across chemistry, biology, physics & climate. Master Transformers, Diffusion Models & GNNs for scientific discovery. 500+ pages, 50+ Colab notebooks. Design molecules, predict proteins, accelerate climate models—all hands-on, zero setup required.

هذا الكتاب هو دليل مختصر وموضّح بالرسوم لأي شخص يرغب في فهم الآلية الداخلية للنماذج اللغوية الضخمة، سواء في سياق المقابلات أو المشاريع أو بدافع الفضول الشخصي.

Have you ever been curious about how your phone unlocks when it sees your face, how a camera can track people and objects in a video, how humans see depth, or how computers can differentiate dogs from cats? This book will start from the basics of image manipulation and build up to cover all of these topics, and more!

Build GPT-2, Llama 3, and DeepSeek from scratch in PyTorch. Every chapter has runnable end-to-end code and loads real pretrained weights. Goes well past where most LLM tutorials stop.

Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

Class imbalance isn’t a problem. Poor methodology is. This book challenges outdated practices and provides rigorous, data-driven alternatives. We focus on selecting the right tools, threshold tuning, real costs (not class frequencies), and strategic evaluation metrics, to build models that work.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.