An Iterative Process for Production-Ready Applications. Author Chip Huyen, co-founder of Claypot AI, considers each design decision - such as how to process and create training data, which features to use, how often to retrain models, and what to monitor - in the context of how it can help your system as a whole achieve its objectives.

A principled model-based approach to computer vision that unifies disparate algorithms, approaches, and topics under the guiding principles of probabilistic models, learning, and efficient inference algorithms.

An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.

A thought-provoking examination of the potential impact of advanced AI on the future of humanity. How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology - and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial.

An exploration of the challenges in aligning artificial intelligence systems with human values and goals.

A collection of case studies showcasing how AI is being applied in various industries and domains.

An exploration of the quest to find the “master algorithm” that can solve all machine learning problems.

A practical guide to implementing machine learning techniques using popular Python libraries.

A thought-provoking examination of the potential risks and implications of advanced artificial intelligence.

A concise and accessible introduction to the core principles and algorithms of machine learning.

An in-depth exploration of the theory and practice of deep learning, a powerful subset of machine learning.

Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.

Suggested reading from Ilya Sutskever, OpenAI’s chief scientist, for John Carmack.

A book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.

This book explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up-to-date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.