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Learning TensorFlow: A Guide to Building Deep Learning Systems
Ebook Free Learning TensorFlow: A Guide to Building Deep Learning Systems
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Product details
Paperback: 242 pages
Publisher: O'Reilly Media; 1 edition (August 27, 2017)
Language: English
ISBN-10: 1491978511
ISBN-13: 978-1491978511
Product Dimensions:
6.9 x 0.8 x 9.1 inches
Shipping Weight: 12.8 ounces (View shipping rates and policies)
Average Customer Review:
3.6 out of 5 stars
24 customer reviews
Amazon Best Sellers Rank:
#344,732 in Books (See Top 100 in Books)
Yet another TensorFlow book that just consolidates the online docs. Seriously, this book is not worth the buying price. There is a one-to-one correspondence with the table of contents and the online docs. I think even some of the paragraphs were pulled straight from the docs. Are you ready for MNIST and Boston housing prices examples all over again? This book is totally uninspired. You are honestly way better off reading the docs and surfing #tensorflow on stackoverflow.
Bunch of copied and pasted code with little explanation. Most of the book is about data-sets and how to parse them. When it comes to neural nets or Tensorflow authors advice the reader to research it on the web. Trying to read this book is like trying to understand Egyptian hieroglyphs.I am just amazed that these three authors were so incredibly lazy that having giving the opportunity to write a book, they decided not to spend even couple of weeks on it. if each of these three authors spend a month on the book, the book could have been something useful. They just decided to take the money and run.The editors Nicole Tache and Shiny Kalapurakkel should be ashamed of themselves for letting a hodgepodge of collected code to be published as a book. And shame on O'Reilly for publishing this.I suspect the positive ratings are by the authors's friends.
This is a bad book in every sense of the word. The authors saw an opening to jump in the deep learning frenzy and make a quick buck by compiling some online material in a volume. Even the formatting is off in this book.Dont buy this book. Go online and find the countless tensorflow tutorials that are available for free.
I'm not that impressed with this book. There's nothing in here that you can't find in the Tensorflow documentation. There's nothing 'informative' in this book at all. The code is poorly documented and really doesn't provide much value.
Really nice, good price
The bad outweighs the good with this title. Deep learning itself receives a brief explanation, but isn't put into any meaningful context. A novice might reasonably wonder what makes deep leaning different from shallow learning, and when to use one versus the other. The answer isn't given by this book. Another Amazon reviewer criticizes the use of the MNIST and CIFAR data sets, which appear in the on-line TensorFlow documentation: I agree: this choice was, to say the least, unimaginative. I suggest that one would do as well to consult on-line references.
As a computer Forensics Specialist, I need to be at least passingly familiar with a gamut of technologies, platforms and applications. I rely on books to bring me up to at least minimum speed. O’Reilly has been a prime go-to source but even they put out a poor product on occasion. “Learning TensorFlow†is poorly written and, as part of a double whammy, poorly edited. One or more of the authors simply doesn’t write well. The prose has all the sophistication of a junior high school paper. I personally find this sentence both clumsy and superfluous: “We will see several practical examples and dive into the details throughout this bookâ€. There is also, to my eyes, an over-abundance of adjectives, a rarity in well-written technical literature. The authors declare that “some basic Python programming know-how, including basic familiarity with the scientific library NumPy†is assumed. They then go on to explain “[m]achine learning concepts are touched upon and intuitively explained throughout the book. For readers who want to gain a deeper understanding, a reasonable level of knowledge in machine learning, linear algebra, calculus, probability and statistics is recommended.†I would substitute “necessary†for “recommendedâ€. The entire book seems to be cobbled together from a variety of sources, with the authors attempting to provide glue to hold them together with what are essentially filler paragraphs, mere strings of words. I won’t pretend to be a subject matter expert – and I don’t think this book would help me achieve that status.Jerry
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