the essence of knowledge
FnT SIG 7:3-4 Deep Learning; Methods and Applications Li Deng and Dong Yu
Deep Learning
Methods and Applications
Li Deng and Dong Yu
Deep Learning: Methods and Applications provides an overview of general deep learning
methodology and its applications to a variety of signal and information processing tasks. The
application areas are chosen with the following three criteria in mind: (1) expertise or knowledge
of the authors; (2) the application areas that have already been transformed by the successful
use of deep learning technology, such as speech recognition and computer vision; and (3) the
application areas that have the potential to be impacted significantly by deep learning and that
have been benefitting from recent research efforts, including natural language and text
processing, information retrieval, and multimodal information processing empowered by multi-
task deep learning.
Deep Learning: Methods and Applications is a timely and important book for researchers and
students with an interest in deep learning methodology and its applications in signal and
information processing.
“This book provides an overview of a sweeping range of up-to-date deep learning
methodologies and their application to a variety of signal and information processing tasks,
including not only automatic speech recognition (ASR), but also computer vision, language
modeling, text processing, multimodal learning, and information retrieval. This is the first and
the most valuable book for “deep and wide learning” of deep learning, not to be missed by
anyone who wants to know the breathtaking impact of deep learning on many facets of
information processing, especially ASR, all of vital importance to our modern technological
society.” — Sadaoki Furui, President of Toyota Technological Institute at Chicago, and
Professor at the Tokyo Institute of Technology
Foundations and Trends
®
in
Signal Processing
7:3-4
Deep Learning
Methods and Applications
Li Deng and Dong Yu
now
now
This book is originally published as
Foundations and Trends
®
in Signal Processing
Volume 7 Issues 3-4, ISSN: 1932-8346.