Introduction to Deep Learning (MIT Press)
Monday, 11 February 2019

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. Author Eugene Charniak, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. The book illustrates the basics of deep learning by working through programs in the Tensorflow open-source machine learning framework

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Author: Eugene Charniak
Publisher: MIT Press
Date: January 2019
Pages: 192
ISBN: 978-0262039512
Print: 0262039516
Audience: Developers wanting to learn deep learning
Level: Intermediate
Category: Artificial Intelligence

 

introdeep

 

 

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Date: January 2017
Pages: 816
ISBN: 978-1119258353
Print: 1119258359
Kindle: B01N12MHZU
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