Mit press dez 2018 (2 Ergebnisse)

Buch. Zustand: Neu. Neuware - An accessible and engaging guide to the atom, the smallest, most fundamental constituent of matter.Until now, popular science has relegated the atom to a supporting role in defining the different chemical elements of the periodic table. In this book, Jack Challoner places the atom at center stage. The Atom investigates the quest to identify the smallest, most fundamental constituents of matter--and how that quest helps us to understand what everything is made of and how it all works. Challoner covers a wide range of topics--including the development of scientific thinking about atoms and the basic structure of atoms; how atomic interactions account for the familiar properties of everyday materials; the power of the atomic nucleus; and what the mysterious quantum realm of subatomic particles can tell us about the very nature of reality. Illustrated in color throughout, The Atom offers clear answers to questions we have all pondered, as well as some we have never even dreamed of. It describes the amazing discoveries scientists have made about the fundamental building blocks of matter--from quarks to nuclear fission to the 'God particle'--and explains them accessibly and concisely. The Atom is the engaging and straightforward introduction to the topic that we didn't get in school.…

Buch. Zustand: Neu. Neuware - A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.…