Books

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

Master language models through mathematics, illustrations, and code―and build your own from scratch!

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.

Unlock the full potential of CatBoost — a powerful gradient boosting library built for structured/tabular data and still underused in practice.In Mastering CatBoost: The Hidden Gem of Tabular AI, you’ll learn how to take advantage of CatBoost’s key strengths: native categorical feature handling, strong accuracy, and fast inference — without brittle preprocessing pipelines.Written for data scientists, ML engineers, and applied researchers, the book covers:Real-world use cases and end-to-end workflowsPractical tuning strategies and diagnosticsInterpretability with SHAP, feature importance, and constraintsDeployment-minded best practices and common failure modesWhether you’re new to CatBoost or ready to go deeper, this is a clear, practical guide to building high-performance tabular models.Early Access is available now, with ongoing updates leading to the full release in 2026.

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.

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.

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.

This book is a quick foray into the world of deep learning-based computer vision and abnormal equipment sound detection. The readers are introduced to the ease with which powerful equipment and product quality monitoring solutions can be built using sound and visual data.

این کتاب راهنمایی مختصر و تصویری است برای افرادی که می‌خواهند سازوکارهای مدل‌های زبانی بزرگ را در مصاحبه‌ها، پروژه‌ها یا حتی برای رفع کنجکاوی شخصی‌شان بهتر درک کنند.

From a single neuron to transformer-based LLMs in production — the deep-learning, NLP, and shipping capstone of the Data Science, End to End series, derived from first principles in Python.

Building production-grade AI systems requires more than just calling an API or running a basic notebook—it demands mastery over hardware constraints, memory bandwidth limits, and distributed inference architectures.​My new book, AI Systems Engineering: From Prototype to Production, is now officially available. It’s a code-first, rigorous engineering manual covering everything from vLLM runtime internals, PagedAttention, and FP8/INT4 quantization to HNSW vector search, hybrid RAG, and autonomous multi-agent swarm architectures.​Stop wrestling with out-of-memory errors and sub-optimal latency. Master the infrastructure that powers modern AI at scale.

Master deep learning with PyTorch & Lightning. Core concepts, practical Q&A, and production-ready code with a full companion GitHub repository.

Понятный иллюстрированный путеводитель по ключевым концепциям и практическим применениям больших языковых моделей. Идеально подходит для работы над проектами, подготовки к собеседованиям и самостоятельного обучения.

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.