Neural Networks

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 Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

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.

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.

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.

What really happens inside a neural network? This book explores the tools and ideas researchers use to uncover how AI systems work, from foundational concepts to practical experiments. Learn to separate solid findings from bold claims, develop useful research skills and prepare for a field that is changing as quickly as the technology itself.

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.

Our starting point was raw CIFAR-10 bytes and a compiler check, and we ended up with residual networks, coroutine pipelines, compressed checkpoints and a real-time classifier built on two cooperating threads.Whenever we introduced a new technique, we gave it a contract, tested it early and measured it honestly. Our concepts rejected the wrong types before a build was finished, and static assertions caught shape mistakes long before training. Every claim in these chapters had an output we could reproduce.

A research monograph connecting quantum annealing, Aizenberg MVN theory and complex spectral surrogate models, with detailed applications to battery energy storage placement in power grids.

AI is changing the way we find and fix software vulnerabilities. Intelligent Discovery explores the techniques behind this shift, from static analysis to AI-driven vulnerability research and automated repair. With a clear-eyed look at both the potential and the pitfalls, it shows what these tools can do, where they fall short and why human expertise still matters.

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.

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

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.

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.