- Hardcover: 800 pages
- Publisher: The MIT Press (Nov. 18 2016)
- Language: English
- ISBN-10: 0262035618
- ISBN-13: 978-0262035613
- Product Dimensions: 17.8 x 2.5 x 22.9 cm
- Shipping Weight: 1.2 Kg
- Average Customer Review: 25 customer reviews
- Amazon Bestsellers Rank: #724 in Books (See Top 100 in Books)
Deep Learning Hardcover – Nov 18 2016
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Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. It provides much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities.(Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX)^
This is the definitive textbook on deep learning. Written by major contributors to the field, it is clear, comprehensive, and authoritative. If you want to know where deep learning came from, what it is good for, and where it is going, read this book.(Geoffrey Hinton FRS, Emeritus Professor, University of Toronto; Distinguished Research Scientist, Google)^
Deep learning has taken the world of technology by storm since the beginning of the decade. There was a need for a textbook for students, practitioners, and instructors that includes basic concepts, practical aspects, and advanced research topics. This is the first comprehensive textbook on the subject, written by some of the most innovative and prolific researchers in the field. This will be a reference for years to come.(Yann LeCun, Director of AI Research, Facebook; Silver Professor of Computer Science, Data Science, and Neuroscience, New York University)
About the Author
Ian Goodfellow is Research Scientist at OpenAI. Yoshua Bengio is Professor of Computer Science at the Université de Montréal. Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.
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The main weakness of this masterpiece is the lack of practical programming exercices left to a companion web site. But to cover all the practical stuff, the book should have exceeded 775 pages that it already has.
I dream of he same content in the form of a series of iPython Notebooks with all exercices and code samples using Keras, TensorFlow and Theano.
[Note] To be completely honest the authors wrote a short disclaimer in the «Machine Learning Basics» chapter 5, page 103 about reinforcement learning. « Such algorithms are beyond the scope of this book ».
"in practice this formula means that"
"This is widely used in the field, because.."
"this was fist proven as a success approach by.."
"this is our short review of the competition results.."
"the intuition behind this is.."
"this was never proven in practice.."
"from the common sense point of view.."
"technologically this is feasible since.."
"this approach allows to fit the batch into RAM memory.."
This books just "mechanically" throws the math expressions at unsuspecting user.
Never attempting to make a conceptual/functional/common sense introduction.
You will not find a single line of Python/R code in the book.
Not a single review of existing NN frameworks/libraries.
You will not find anything about technology and/or methodology of data collection, data cleaning, outliers detection, practical hints on training etc
Never trying to connect to any publications (i.e. other than the authors) in the field.
Never ever bringing the meaning of the formulas into discussion.
Deservedly, 3 out 5.
Plus all , Fast shipping in just 3 days and packed excellent without even a scratch on it. It mentioned that is used like new and it is completely new with less than half price. I suggest to buy books from this seller. Also the book is in colour not W&B.
The quality of papers is a little poor but it is acceptable for me with such amazing price.
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