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NehaJ Feb 25, 2018

Awesome book for starters and also for the the ones who wants to refresh their basics. This books start with basics with systematic explanation through code examples. The great thing about the book is you don't find it boring at any point of time and your interest keep on growing as you go to every next page and working example. Liked last chapter the most which has latest of deep learning examples from field like bio informatics.An awesome read for deep learning enthusiast!!!!

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J. Pegg Aug 10, 2018

I've found the information to be good, but the book could really benefit from more proofreading. Most of the issues are grammatical (which makes me believe the general editor should have read it more closely), although there are also areas where concepts and ideas are used before they're properly introduced (which makes me believe the content reviewers should have been a bit more critical, or perhaps have slightly less domain knowledge). Again, this is a good book that covers many complex topics and assumes a technical reader (which I appreciate). I found it very informative and useful, especially when I wasn't being annoyed by simple errors or turning to Wikipedia to define concepts.

Anaxagoras Jul 26, 2018

This is a great introduction to DL. Covers all the bases. Lots of tips, tricks and applications.One drawback is that nothing is covered in enough detail to truly implement a production system.Based on the level of detail, I would give this book three stars. Based on the content, I would give this book 5 stars. My rating averages the score for the content and coverage.There doesn’t appear to be a single book that covers everything in enough detail to implement a production system for both natural language processing and computer vision systems.The ImageNet bundle from pyimagesearch has everything you need to build a deep learning system for computer vision applications but the cost could be prohibitive for some. OTOH a box with four GTX 1080 TIs, 128Gb ram, a decent CPU, and the rest of the parts could easily cost $6,000. $600 for books and code is just another 10% relative to the cost of a DL box.This won’t be the only DL book you read. I’ve read most of the books on the market. My favorites include Chollet’s Keras book and Geron’s Tensorflow book as well as Adrian Rosebrock’s books which use Keras and MXNet. This one is a very useful addition to my library.You will need to read this book and at least two of Chollet, Geron, and Rosebrock to have a reasonable grasp of the important concepts for DL for computer vision. Arxiv-sanity preserver is a great way to keep up to date on the research literature.It would be helpful to have a working knowledge of Docker as well.I’m looking forward to finding a good book on PyTorch to complement my knowledge of DL frameworks.

Amrita Dev Feb 19, 2018

The book is very well written which covers the basic along with sample code which is easy to follow. The books starts with basic algebra and example and then goes deep in the Deep Learning space.

stephane Mar 04, 2024

interesting but unfortunately the examples are for tensorflow v1 . they don't work , tensorflow v1 is deprecated

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Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling

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