Pt. 1. Universe. Who are we? Where are we? -- pt. 2. Human immortality and future human evaluation.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization.

Now fully updated, this bestselling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help users gain an intuitive understanding of the concepts and tools for building ...

This book presents some of the most important modeling and prediction techniques, along with relevant applications.

Three main themes are discussed in this book: concepts, working principles, and mechanics of deployable structures, both in engineering and biology; in addition: theory of foldable bar structures and application to deployable tensegrieties; ...

"Describes the quest to find the Master Algorithm, which will take machine learning to the next level, allowing computers to learn how to solve not just particular problems but any problem, "--Novelist.

The third edition of this definitive and popular book continues to pursue the question: what is the most efficient way to pack a large number of equal spheres in n-dimensional Euclidean space?

The input descriptions will specify what the user wants dome rather than how to do it. This book discusses a central problem in the development of autonomous robots.

The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models.

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.