Machine Learning
- Super Study Guide: Transformers & Large Language ModelsAfshine Amidi and Shervine Amidi
A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal 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.
- The Hundred-Page Machine Learning BookAndriy Burkov
Everything you really need to know in Machine Learning in a hundred pages.
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.
- Mastering CatBoost: The Hidden Gem of Tabular AIHarness the Power of CatBoost for Tabular Data and BeyondValery Manokhin
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.
- A Hands-On Guide to Fine-Tuning Large Language Models with PyTorch and Hugging FaceDaniel Voigt Godoy
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.
- Practical C++ Machine LearningHands-on strategies for developing simple machine learning models using C++ data structures and librariesGitforGits | Asian Publishing House
My goal is to equip other programmers with the confidence to confidently incorporate machine learning into their C++ projects by guiding them through real-world examples and addressing common challenges head-on.
- Interpreting Machine Learning Models With SHAPA Guide With Python Examples And Theory On Shapley ValuesChristoph Molnar
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.
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.
- Hacker's Guide to Machine Learning with PythonHands-on guide to solving real-world Machine Learning problems with Scikit-Learn, TensorFlow 2, and KerasVenelin Valkov
This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery!
- Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive MaintenanceFrom Data to Process InsightsAnkur Kumar and Jesus Flores Cerrillo
This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions. Also available at Google Play
- Super Study Guide: Dönüştürücüler ve Büyük Dil ModelleriAfshine Amidi, Shervine Amidi, and Merve Ayyüce KIZRAK
Büyük dil modellerine dair ana kavramları ve pratik uygulamaları kapsayan, açık ve görsellerle desteklenmiş bir rehber. Projeler, mülakatlar ve kişisel öğrenme için idealdir.
- My Adventures with Large Language ModelsBuild foundational LLMs from Transformers to DeepSeek, from scratch, in PyTorch.Prathamesh S.
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.
市面上的深度學習書要么晦澀難懂，要么是平台使用說明書，只講理論缺少實現或者只有編程缺少原理講解，都很難讓人理解深度學習的基本原理，本書採用理論講解與代碼實現的方式深入淺出地剖析了深度學習的技術原理和實現細節，教會讀者如何從零編寫一個深度學習庫。內容包含了：梯度下降法、回歸學習、前饋神經網絡、卷積神經網絡、循環神經網絡、生成網絡。
- Physics-Informed Machine Learning A Practical Guide: Stage 1 Foundations & Your First PINNKompri Kompri
A practical, code-first guide to physics-informed machine learning for scientists and engineers. You'll build a working Physics-Informed Neural Network from scratch in PyTorch, solve a real differential equation, and verify it against the exact solution — staying honest about both the power and the limits of the method.
