This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

This book covers both classical and modern models in deep learning.

In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).

This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians.

Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques.

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible ...

Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in ...

While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data ...

The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.