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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.
For more information about the book, please visit https://mitpress.mit.edu/books/deep-learning
|Call for Nominations: Distinguished Industry Speakers and Distinguished Lecturers||31 May 2020|
|The First TReNDS Neuroimaging Competition is Live!||29 June 2020|
|Call for Nominations: Chair, Women in Signal Processing Committee and Chair, Young Professionals Committee||10 July 2020|
|ALASKA 2 Steganalysis Challenge is Open||13 July 2020|
|Nominations Open for 2020 SPS Awards||1 September 2020|
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