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.

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.

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

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

Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

This book is meant to discover how the language Rust, which is known for being fast and safe, can work with the constantly changing field of machine learning, neural networks and deep learning. The datasets of COVID, CIFAR-10 and practical examples are meant to show how Rust's features can be used to solve problems that come up in machine learning.

Revised for PyTorch 2.x! In 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.

This book explores supervised machine learning for scientific research, addressing its limitations in interpretability, causality, and uncertainty quantification. By unifying philosophical justification with practical solutions, it provides a roadmap for turning machine learning into a rigorous tool for science.

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.

Unlock NVIDIA GPU performance from Rust. This practical guide takes you from GPU architecture and memory hierarchies to writing and optimizing real CUDA Rust kernels. Explore both official Rust CUDA approaches with runnable examples and learn how to build fast, production-ready GPU code without giving up Rust’s safety and clarity.

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

A chatbot can describe how to do your work. An agent can do it—if you build it to be trusted. Learn to construct, verify, and deploy AI agents that earn a place in real scientific and engineering practice.