Deep 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.
Master language models through mathematics, illustrations, and code―and build your own from scratch!
- Mastering Modern Time Series ForecastingA Comprehensive Guide to Statistical, Machine Learning, and Deep Learning Models in PythonValery Manokhin
800 pages. 11 chapters. The full forecasting stack in Python — from ARIMA to foundation models — with production-grade code and proper evaluation. No hype.
- Machine Learning with Rust, Second EditionImplement data pipelines, classical models, deep learning and NLP using burn, candle, linfa and smartcoreGitforGits | Asian Publishing House
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.
- Super Study Guide: Transformer 与大语言模型Afshine Amidi, Shervine Amidi, Tao(Thomas) Yu, and Binbin XiongNo Description Available
- Probabilistic Forecasting with Conformal Prediction in PythonThe Practical Guide to Uncertainty Quantification for Data Science, Machine Learning, and ForecastingValery Manokhin
Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.
- Applied Conformal Prediction:Practical Uncertainty Quantification for Real-World MLPractical Uncertainty Quantification for Real-World ML Learn Conformal Prediction (CP), the state-of-the-art technique for building statistically valid, model-agnostic prediction intervalsValery Manokhin
A powerful new book on Conformal Prediction by bestselling author and machine learning expert Valery Manokhin, bridging theory and real-world machine learning. Discover how to quantify uncertainty with statistical guarantees—across deep learning, time series, forecasting, and more. Preorder now before the price goes up.
- 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.
- Super Study Guide: Transformer と大規模言語モデルAfshine Amidi, Shervine Amidi, and Yoshiyuki Nakai
大規模言語モデル (LLM) の主要な概念から実践的な応用例まで、簡潔に図解されている学習ガイドです。プロジェクトでの利用、面接対策、個人的な学習にも最適です。
- Super Study Guide: المحولات والنماذج اللغوية الضخمةShervine Amidi, Afshine Amidi, and Serry Sibaee
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.
- Neural Networks with Python, Second EditionExplore Transformers, ViTs, Diffusion, KANs, and SSMs using Python, NumPy and PyTorchGitforGits | Asian Publishing House
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.
- Super Study Guide: Transformers y Grandes Modelos de LenguajeAfshine Amidi, Shervine Amidi, laramaktub, and Steven Van Vaerenbergh
Este libro es una guía concisa e ilustrada para cualquiera que desee comprender el funcionamiento interno de los Grandes Modelos de Lenguaje, ya sea de cara a realizar entrevistas, proyectos o para satisfacer su curiosidad.
- Structure and Implementation of Agentic ProgramsA Practical Guide to Building Intelligent LLM Applications with PythonGuillaume Saupin
Stop building brittle LLM wrappers and start engineering production-grade autonomous agent networks.This hands-on guide takes you from fundamental Transformer mechanics to orchestrating multi-agent architectures under Kubernetes with Python. Step beyond basic prototypes to master graph-based orchestration, grammar-constrained outputs, LoRA fine-tuning, RAG, and MCP. Learn to navigate critical production trade-offs—model routing, quantization, cost control, prompt injection security, and human-in-the-loop monitoring—to deploy resilient, enterprise-ready AI systems.
- Python Simplified with generative aiA beginner to pro journey for students professionals and developersAnshuman Mishra
Learn Python. Build AI. Create the Future.What if you could write Python programs that generate content, answer questions, create code, summarize documents, and power intelligent applications?Python Simplified with Generative AI takes you on a complete journey from Python basics to advanced AI-powered development.Inside this book, you will learn:✔ Python Programming from Scratch✔ Data Structures and Object-Oriented Programming✔ AI and Machine Learning Foundations✔ Generative AI Concepts and Applications✔ Prompt Engineering Techniques✔ GPT-Powered Text Generation✔ AI Chatbots and Virtual Assistants✔ Image Generation with AI APIs✔ Flask, FastAPI, Streamlit, and Gradio Development✔ Real-World AI Projects for Your PortfolioWhether you are a student, professional developer, freelancer, educator, or entrepreneur, this book will help you transform ideas into intelligent applications and prepare for the next generation of software development.The future belongs to developers who can combine programming with artificial intelligence. Start building that future today.
I wanted to understand how ChatGPT and other large language models (LLMs) really work, so I read a lot of books, watched YouTube videos, asked hundreds of questions, and wrote it all down. This book is the result. If you want to understand how large language models like ChatGPT actually work, from tokens and vectors to transformers and training, this book will explain it in a clear, approachable way.
