Machine Learning

A machine can hand you a grouping that is confident, stable, internally consistent, and seven times worse than the one it found a moment earlier. Nothing in the output says so.This book works k-means through completely on twelve bakery customers, small enough that every number is printed and checkable. You will build the method from nothing, see exactly where it succeeds, and see exactly where it fails silently.Fifty-eight figures, every one captured from a running implementation.

Every deep learning tutorial shows you the code. Almost none show you the idea. This is the book to read first - before the frameworks, before the maths notation, before the course you've already paid for. One example, followed all the way through, from a random guess to a network that works. By the end you'll understand what backpropagation actually does, why gradients matter, and what's really happening while your model trains. No calculus required.

Prompting has more advice than evidence. This book measures it instead: a model reading apattern it has never seen, the same model failing at a task in one pass and solving itperfectly in four, a decoder that guarantees a format instead of improving the odds, and amodel that is 2.4% accurate while 78.6% confident. Six central claims are built from scratch and measured on a single CPU core. Everythingtaken from published research sits in a grey box marked "Not measured here" — so you neverhave to guess which is which.

Five certifications ask about the same twenty topics, and each one stops at a differentdepth. This book teaches the topic once, properly, then shows you exactly where each examstops — with 61 original practice questions where every wrong option is explained. Every number in it was measured. Every line of code was run. The code is included.

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 This book challenges outdated practices and provides rigorous, data-driven alternatives.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.

From your first JAX script to large-scale AI systems, this book shows how to write faster, cleaner Python for modern computing. Learn the ideas behind JAX through practical examples, real projects and clear explanations that help you build everything from scientific simulations to distributed machine learning.

Learn how to design, build, and deploy reliable, scalable, and maintainable Machine Learning systems in production through an iterative end-to-end process.

Build a Stronger Technical Brand. Write Better Code. Publish with Confidence.The Official Ahmed Adawy Tech Capsules Guide is a practical resource for creating clear, consistent, and professional technical content. Learn how to structure technical explanations, present code examples more effectively, maintain technical accuracy, and build a recognizable identity for your educational and publishing projects.

Every news feed is full of AI buzzwords: Transformers, tokens, embeddings, context windows, hallucinations, objective functions. Yet most explanations are either dense academic textbooks or empty marketing fluff. ☕ Coffee Break AI is your practical guide to AI. Written in plain English with warm real-world analogies. It breaks down the core mechanisms of AI into 40 bite-sized chapters.

If you want to build real NLP applications instead of just experimenting with notebooks, spaCy is one of the best places to start. This book walks you through the entire journey from the fundamentals to advanced production workflows with practical explanations, real code examples and hands-on projects that show you how to build fast, reliable NLP systems for the real world.

We'll stick to five libraries, not because more would be a problem, but because keeping it simple shows how well we can organise things. When you download MNIST with just the standard library, you finally see what a dataset loader was hiding. If you write attention as four lines of NumPy before you ever call a PyTorch module, it's no longer a magic process but just plain arithmetic.

Large language models are changing the world, yet few people understand how they actually work. This book cuts through the hype, explaining the ideas behind modern AI with clarity, precision, and no unnecessary jargon.

PyTorch Deep Dive is a practical guide to mastering modern deep learning with PyTorch. From core concepts to advanced topics like transformers, diffusion models, and production deployment, it combines clear explanations, hands-on examples, and real-world best practices to help you build and scale AI applications with confidence.

Make your Python AI code up to 100× faster using NumPy vectorization, Numba, parallel execution, and GPU acceleration. Learn through practical benchmarks and real-world optimization examples.

Run powerful AI models directly in the browser with no servers and no cloud dependencies. LiteRT.js teaches you how to build fast, private, and production-ready machine learning applications using WebAssembly, WebGPU, and WebNN, with practical examples and performance-focused techniques throughout.