An introduction to a broad range of topics in deep learning covering mathematical and
conceptual background deep learning techniques used in industry and research perspectives.
"Written by three experts in the field Deep Learning is the only comprehensive book on the
subject.” —Elon Musk cochair of OpenAI cofounder and CEO of Tesla and SpaceX Deep learning is
a form of machine learning that enables computers to learn from experience and understand the
world in terms of a hierarchy of concepts. Because the computer gathers knowledge from
experience there is no need for a human computer operator to formally specify all the
knowledge that the computer needs. 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. The text
offers mathematical and conceptual background covering relevant concepts in linear algebra
probability theory and information theory numerical computation and machine learning. It
describes deep learning techniques used by practitioners in industry including deep
feedforward networks regularization optimization algorithms convolutional networks sequence
modeling and practical methodology and it surveys such applications as natural language
processing speech recognition computer vision online recommendation systems bioinformatics
and videogames. Finally the book offers research perspectives covering such theoretical
topics as linear factor models autoencoders representation learning structured probabilistic
models Monte Carlo methods the partition function approximate inference and deep generative
models. Deep Learning can be used by undergraduate or graduate students planning careers in
either industry or research and by software engineers who want to begin using deep learning in
their products or platforms. A website offers supplementary material for both readers and
instructors.
