Explore various possibilities with deep learning and gain amazing insights from data using Google’s brainchild-- TensorFlow
Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide
Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environment

Description

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

Who is this book for?

If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed.

What you will learn

Set up your computing environment and install TensorFlow
Build simple TensorFlow graphs for everyday computations
Apply logistic regression for classification with TensorFlow
Design and train a multilayer neural network with TensorFlow
Intuitively understand convolutional neural networks for image recognition
Bootstrap a neural network from simple to more accurate models
See how to use TensorFlow with other types of networks
Program networks with SciKit-Flow, a high-level interface to TensorFlow

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Amazon buyer Sep 28, 2018

Practical approach

Amazon Verified review

Maxwell B. Anselm May 11, 2018

If you've done some reading about machine learning and already know the gist of how neural networks work, this book will get you up to speed with some simple, practical examples. It's very light and handwavy on the theory, so I wouldn't recommend it to someone who is completely fresh to the topic. But I liked that it took a very hands-on approach with the code examples, explaining every line and generally doing things "the hard way" so that you actually felt like you were in control of the networks you set up.I followed along using the latest TensorFlow libraries available on Arch Linux and I was surprised that the examples were already a little out of date. I got quite a few deprecation warnings and one example was straight up broken because of outdated syntax, but it was easy to figure out how to fix everything.I happen to know that the author originally presented the material as a video series and this book was transcribed by a third party and... unfortunately, it shows. There are some weird wordings that I can only assume were incorrectly transcribed, and some of the text refers to code examples that must be downloaded separately as if they're right there in the text. But again, nothing got in the way of understanding the material.

Dimitri Shvorob Mar 11, 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 and 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. One week later, I am still not done, but some options can already be discarded."Hands-On Deep Learning with TensorFlow" is one of them. The book is the thinnest of the bunch; with just 174 Packt pages - equivalent to under 100 of "regular" ones - to play with, it cannot really be a TensorFlow reference, only a (sketchy) TensorFlow introduction. In this case, page count is kept down by (with one exception) focusing on a single problem, MNIST character recognition. Despite a recent release date, the book does not cover the higher-level APIs of Estimators and Datasets, and adopts the "old school", low-level approach. It is really not bad, and does add value to the doc (for release 1.3 or so) and online treatments of "TensorFlow vs. MNIST", but the truth is, for $35, you can find something more substantial. Consider "Hands-On Deep Learning with TensorFlow" if you see it on sale.

Lipin Pius Feb 28, 2018

Please don't buy this book if you're looking for some good learning material

Hands-On Deep Learning with TensorFlow: Uncover what is underneath your data!

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