This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

The first edition of ELL (1993, Ron Asher, Editor) was hailed as "the field's standard reference work for a generation".

Graphics in this book are printed in black and white.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks.

With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ...

“Exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it.” —John Horgan “If you want to know about AI, read this book.

The Lexicon can be used for introductory and advanced courses, and includes a range of exercises and in-class activities designed to engage students, and help them acquire the knowledge and skills they need.

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

This is the latest addition to a group of handbooks covering the field of morphology, alongside The Oxford Handbook of Case (2008), The Oxford Handbook of Compounding (2009), and The Oxford Handbook of Derivational Morphology (2014).

In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications.