Books
Master language models through mathematics, illustrations, and code―and build your own from scratch!
- 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.
- 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.
- 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.
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.
- Data Visualization Using Tableau for Data ScientistsSameh Shamroukh
Turn data into decisions. Data Visualization Using Tableau for Data Scientists shows how interactive visuals, analytics, and storytelling come together to make complex data understandable, actionable, and impactful.
- 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.
- Generative AI with local LLMA comprehensive roadmap for building AI-Driven applications with local LLMsShamim Bhuiyan and Timur Isachenko
Learn how to build your own AI application step-by-step. A hands-on guide to AI development with local LLM inference
- Database and sql for data scienceAnshuman Mishra
Why This Book Is Unique· Focused specifically on data science applications of SQL, not just traditional database operations.· Includes Python integration, bridging database skills with modern data analysis.· Covers NoSQL and unstructured data, expanding student exposure beyond relational databases.· Emphasizes real datasets, case studies, and hands-on exercises, making learning interactive and practical.· Prepares students for academic projects, internships, and entry-level data science roles.
Master deep learning with PyTorch & Lightning. Core concepts, practical Q&A, and production-ready code with a full companion GitHub repository.
- From Stochastic Chaos to Deterministic CertaintyGrigorios Agathangelidis
Stop choosing between an AI that's powerful and one you can certify. From Shannon's entropy to a reproducible, auditable Industry Language Model — built in C#, wrapped in Deterministic Islands, with every number independently verified.
- Coffee Break AIUnderstand How Artificial Intelligence Really Works - One Short Chapter at a TimeFinxter
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.
- 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.
- 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.
