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!

800 pages. 11 chapters. The full forecasting stack in Python — from ARIMA to foundation models — with production-grade code and proper evaluation. No hype.

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.

Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.

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.

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.

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.

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.

Ditch slow, expensive AI APIs and bring the power of Large Language Models directly to your users' browsers. This guide teaches JavaScript and TypeScript developers how to build private, offline-capable, and blazing-fast AI applications. Master the local-first AI stack with Transformers.js, WebGPU, and Ollama to slash costs and own your data. Become a leader in the new era of serverless AI and deliver an instantaneous user experience

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

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.

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.

AIエージェントの構築が、これほど容易だった時代はない。そして、実際に機能するものを作ることが、これほど難しい時代もない。本書は言語モデルの基礎から本番対応マルチエージェントシステムまで、失敗が起こる前に予測し、壊滅的な障害ではなく優雅な劣化を設計し、完全なアーキテクチャの所有権を確立するための深さをもって、あなたを導く。ペーパーバック版はamazonにて好評発売中。

Artificial Intelligence is powered by neural networks.But how do neural networks actually learn?How do systems recognize faces, understand speech, generate text, and create images?Neural Networks and Architectures: A Comprehensive Guide for Students provides a complete learning pathway from fundamental concepts to state-of-the-art deep learning architectures.Inside this book, you will discover:✓ Biological inspiration behind neural networks✓ Mathematical foundations of deep learning✓ Perceptrons and Multi-Layer Perceptrons (MLPs)✓ Forward Propagation and Backpropagation✓ Activation Functions and Regularization Techniques✓ Convolutional Neural Networks (CNNs)✓ Recurrent Neural Networks (RNNs), LSTM, and GRU✓ Autoencoders and Generative Adversarial Networks (GANs)✓ Transformer Architecture, BERT, and GPT✓ Optimization Algorithms and Model Training✓ Practical Projects using TensorFlow, PyTorch, and Keras✓ Ethical Considerations and Future Research DirectionsDesigned for students, educators, researchers, and AI enthusiasts, this book combines theory, mathematics, implementation, and applications into one comprehensive learning resource.Build a strong foundation in neural networks and prepare yourself for the future of Artificial Intelligence.