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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Modern Frontend Development with Node.js

Author: Florian Rappl
Publisher: Packt Publishing
Date: November 2022
Pages: 208
ISBN: 978-1804618295
Print: 1804618292
Kindle: B0B9BH5WBS
Audience: Node.js developers
Level: Introductory/Intermediate
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Author: Brad Williams, Justin Tadlock, John James Jacoby
Publisher: Wrox
Pages: 480
ISBN: 978-1119666943
Print: 1119666945
Kindle: B0899MW9CP
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Rating: 4.5
Reviewer: Kay Ewbank

The authors of this book are well-known in the WordPress world, with more than 100 published plug [ ... ]


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