- Paperback: 536 pages
- Publisher: Packt Publishing Limited (12 December 2017)
- Language: English
- ISBN-10: 1788293592
- ISBN-13: 978-1788293594
- Product Dimensions: 19 x 3.1 x 23.5 cm
- Average Customer Review: 2 customer reviews
- Amazon Bestsellers Rank: #1,36,625 in Books (See Top 100 in Books)
TensorFlow 1.x Deep Learning Cookbook Paperback – Import, 12 Dec 2017
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About the Author
Antonio Gulli is a transformational software executive and business leader with a passion for establishing and managing global technological talent for innovation and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and manage teams in six different countries in Europe and America. Currently, he works as site lead and director of cloud in Google Warsaw, driving European efforts for Serverless, Kubernetes, and Google Cloud UX. Previously, Antonio helped to innovate academic search as the vice president for Elsevier, a worldwide leading publisher. Before that, he drove query suggestions and news search as a principal engineer for Microsoft. Earlier, he served as the CTO for Ask.com, driving multimedia and news search. Antonio has filed for 20+ patents, published multiple academic papers, and served as a senior PC member in multiple international conferences. He truly believes that to be successful, you must have a great combination of management, research skills, just-get-it-done, and selling attitude. Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi. She has been actively teaching neural networks for the last 20 years. She did her master's in electronics in 1996 and PhD in 2011. During her PhD, she was awarded the prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She had been awarded the best presentation award at International Conference Photonics 2008 for her paper. She is a member of professional bodies such as OSA (Optical Society of America), IEEE (Institute of Electrical and Electronics Engineers), INNS (International Neural Network Society), and ISBS (Indian Society for Buddhist Studies). Amita has more than 40 publications in international journals and conferences to her credit. Her present research areas include machine learning, artificial intelligence, neural networks, robotics, Buddhism (philosophy and psychology) and ethics in AI.
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"Machine Learning with TensorFlow" by Shukla, published by Manning in 2018-02, 272 pp, $43
"Mastering TensorFlow 1.x" by Fandango, Packt, 2018-01, 474 pp, $35
"Pro Deep Learning with TensorFlow" by Pattanayak, Apress, 2017-12, 398 pp, $37
"TensorFlow 1.x Deep Learning Cookbook" by Gulli and Kapoor, Packt, 2017-12, 536 pp, $32
"Neural Network Programming with TensorFlow" by Ghotra and Dua, Packt, 2017-11, 274 pp, $40
"Predictive Analytics with TensorFlow" by Karim, Packt, 2017-11, 522 pp, $50
"Machine Learning with TensorFlow 1.x" by Hua and Azeem, Packt, 2017-11, 304 pp, $39
"Learning TensorFlow" by Hope and Resheff, O'Reilly, 2017-08, 242 pp, $25
"Hands-On Deep Learning with TensorFlow" by Van Boxel, Packt, 2017-07, 174 pp, $35
"Deep Learning with TensorFlow" by Zaccone, Karim, Menshawy, Packt, 2017-04, 320 pp, $50
"TensorFlow Machine Learning Cookbook" by McClure, Packt, 2017-02, 370 pp, $30
"Building Machine Learning Projects with TensorFlow" by Bonnin, Packt, 2016-11, 291 pp, $35
"Getting Started with TensorFlow" by Zaccone, Packt, 2016-07, 180 pp, $35
I reviewed the doc on tensorflow.org - including the doc for older releases - then started looking at books.
Two weeks later, I am still not done - the book by Shukla has not arrived - but the picture is reasonably clear. The books by Zaccone, Karim, Zaccone and Karim, Bonnin, Hua and Azeem, Ghotra and Dua, and (probably) Van Boxel, can be skipped. (See my reviews of those titles for detail). The remaining five choices fall into four clusters. First, the book by Hope and Resheff provides a good-quality introduction to TensorFlow. Second, the book by Pattanayak unexpectedly goes for academic rigor - the book's subtitle refers to "mathematical foundations" - and emerges as a textbook about the algorithms associated with TensorFlow. The third cluster is formed by the books by Fandango and Gulli and Kapoor - both are unpolished but serviceable, substantial books which go for wide coverage. Finally, McClure's book sits between Clusters 1 and 3.
If I want to continue this elimination game, "Fandango vs. Gulli and Kapoor" is an obvious match-up - and Fandango comes out on top, although only on points. Fandango's book is thinner, but covers more topics - if I had to put a number on it, I would say it covers 10-15% more. G&K's seem to have a markedly superior coverage of CNNs - but that topic is just not my cup of tea. Writing is very similar across the two books. In the end, I pick Fandango and move on, but you may sensibly decide otherwise.
Of course I get the book and go straight for that chapter, and wait for it... he is using Keras to do it.
The title makes it seem like its a book on tensorflow but the one chapter that i read (chapter 4 section on using pretrained models) is using keras!
There are trivial errors in the code section of the book, where variable names are mangled through elementary cut and paste errors or bad formatting.
There are formatting errors in the code sections of the book. This is TensorFlow, therefore Python, and whitespace and indenting in Python matters. But that doesn't stop the authors and typesetters from interspersing prose comments in between indented sections of code in a way that utterly confuses the whitespace.
There are serious errors in the code section of the book, where even once you figure out what the variable name is supposed to be, still fail to execute because the overall concept is Just Plain Wrong.
There are errors in the prose part of the book, some easily understood, some painfully awkward, and some which will cause the reader to stop and puzzle out exactly what is being said.
There are organization errors in the prose where, for instance, the authors say something like, "There are three ways to do such-and-such," then give poorly explained examples in a numbered list, then barrel right on in that numbered list for totally unrelated points four, five and six. No, really-- it's a marvel to behold. I promise you, no one read this book between delivery of the raw manuscript and final publication. Not one person. Just like no one actually ran the code in this book.
Once we get to meaningful examples, the examples are woefully incomplete. Some-- not all-- of the code is available on the author's private GitHub page, but not all of it, and major pieces of exposition are missing. How does one actually obtain a particular CSV file used in the example, for instance? Well, you can get it from the GitHub page, but this is nowhere explained. Or maybe they keep calling it a CSV file but you're supposed to use the raw, non-CSV raw file that they link to from another site entirely. Who can know? Not you, not from reading this book!
Even the typesetting is atrocious-- in addition to the indenting/whitespace issues above, there are occasional snippets of mathematical notation, and in the PDF, at least, they look like someone took very low resolution bitmap graphics and jammed them into the text looking pixelated and sad. This is what we've come to: A book so terrible that the deficiencies of the typesetting are relevant.
This book is trash and morally speaking these huckster publishers owe me more than my original five dollar investment back as payment for my stolen time.