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Your quick guide to implementing TensorFlow in your day-to-day machine learning activities

Learn advanced techniques that bring more accuracy and speed to machine learning

Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow

Description

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

What you will learn

Become familiar with the basics of the TensorFlow machine learning library

Get to know Linear Regression techniques with TensorFlow

Learn SVMs with hands-on recipes

Implement neural networks and improve predictions

Apply NLP and sentiment analysis to your data

Master CNN and RNN through practical recipes

Take TensorFlow into production

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Dimitri Shvorob Mar 24, 2018

Wishing to learn about TensorFlow, I decided to survey TF books available from Amazon, and pick one or two for further study. I excluded self-published offerings, and ended up with this longish list, dominated by Packt titles:"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, $35I reviewed the doc on tensorflow.org - including the doc for older releases - then started looking at books.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, but very oddly composed - a good start, and then we get lost in the weeds - 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. (Great, but not what I am looking for). 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.Gulli and Kapoor's worthwhile book is edged out by Fandango's, and I think Fandango's book narrowly "dominates" McClure's too. I like McClure's (a) somewhat better writing, (b) better coverage of regression, (c) attention to SVMs and clustering, both no-shows in Fandango's book. On the other hand, the 100-Packt-pages-thicker book by Fandango has a few topics not in McClure's, including the usual suspects - reinforcement learning, GANs, distributed computation - and more unusual ones, like TensorFlow debugging. McClure's weak introduction to the perceptron in Chapter 6 sealed the deal - I pick Fandango's book and await Shukla's.

Amazon Verified review

Thomas Feb 19, 2018

Pros:- Treatment of machine learning algorithms other than deep learning.- Quick intro to TF.Cons:- Coding style is not great. Many redundant codes.For example, sess.run can actually return multiple variables from the computational graph and I don't understand why the author used it such as way that sess.run only returns one variable and then copy and paste the code for different variables.- There are some horrible typos and conceptual mistakes.For example:pg 103, the RBF kernel is written wrongly.pg 85, the normalization of features is done wrongly, where the training set and test set are normalized independently. Ideally, the training set should be normalized and the same scale factor fitted using the training set is used to normalize the test set too.Overall, it is a great effort, but much more editing and careful representation of the ideas are needed. Also, not a good place for total beginners to the field to use this book due to the errors and many conceptual gaps that are not filled in.

Amazon Verified review

Bibliophage Dec 08, 2017

If you're a beginner, this book will confuse you. If not, it will frustrate and annoy you. (You may even write a cranky review.) It reads like a rough draft, with hundreds of typos, weird punctuation, and unparsable sentences. Some of the code snippets have syntax errors. Many of them are irrelevant in context but would clarify a point made elsewhere. (cut-and-paste errors?)Every book starts as a draft. This is good work, but it’s a copy edit and tech review away from being a good book. Those are the publisher’s responsibility, not the author’s. Packt dropped the ball.

Amazon Verified review

Mithun Patel Dec 07, 2017

Great book

Amazon Verified review

Jorge Nov 01, 2017

Just started going through the pages and there are lots of typos, both in the code and the main text. Poorly written.

Amazon Verified review

TensorFlow Machine Learning Cookbook: Over 60 practical recipes to help you master Google's TensorFlow machine learning library

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