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Deep Learning with TensorFlow
Deep Learning with TensorFlow

Deep Learning with TensorFlow: Explore neural networks with Python

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Profile Icon Zaccone Profile Icon Milo Profile Icon Karim Profile Icon Menshawy
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€41.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (10 Ratings)
Paperback Apr 2017 320 pages 1st Edition
eBook
€28.99 €32.99
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€41.99
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Arrow left icon
Profile Icon Zaccone Profile Icon Milo Profile Icon Karim Profile Icon Menshawy
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€41.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (10 Ratings)
Paperback Apr 2017 320 pages 1st Edition
eBook
€28.99 €32.99
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Deep Learning with TensorFlow

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Key benefits

  • Learn how to implement advanced techniques in deep learning with Google’s brainchild, TensorFlow
  • Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide
  • Real-world contextualization through some deep learning problems concerning research and application

Description

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.

Who is this book for?

The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.

What you will learn

  • *Learn about machine learning landscapes along with the historical development and progress of deep learning
  • *Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x
  • * Access public datasets and utilize them using TensorFlow to load, process, and transform data
  • * Use TensorFlow on real-world datasets, including images, text, and more
  • * Learn how to evaluate the performance of your deep learning models
  • * Using deep learning for scalable object detection and mobile computing
  • *Train machines quickly to learn from data by exploring reinforcement
  • learning techniques
  • *Explore active areas of deep learning research and applications
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 24, 2017
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781786469786
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Google
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Product Details

Publication date : Apr 24, 2017
Length: 320 pages
Edition : 1st
Language : English
ISBN-13 : 9781786469786
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Table of Contents

10 Chapters
Getting Started with Deep Learning Chevron down icon Chevron up icon
First Look at TensorFlow Chevron down icon Chevron up icon
Using TensorFlow on a Feed-Forward Neural Network Chevron down icon Chevron up icon
TensorFlow on a Convolutional Neural Network Chevron down icon Chevron up icon
Optimizing TensorFlow Autoencoders Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
GPU Computing Chevron down icon Chevron up icon
Advanced TensorFlow Programming Chevron down icon Chevron up icon
Advanced Multimedia Programming with TensorFlow Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon

Customer reviews

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Verified Amazon Customer Jul 22, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book. It contains lots of practical examples. However, when I copied the codes from the book, there were some alignment issues and some codes were not executed correctly. Therefore, I had to manually fixed them.Fortunately, the authors have fixed all the minor bugs and the updated codes that I have downloaded from GitHub/Deep-Learning-with-TensorFlow are working perfectly.
Amazon Verified review Amazon
Ambuj Jan 07, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good introduction if you want to start with tensor flow
Amazon Verified review Amazon
Matthew R. Versaggi Jan 03, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I had such high hopes for this book - the theory is OK, but this is supposed to be a practitioners book with code that actually works and is of Tensorflor 1.X whereas there is lots of code pre-tensorflow 1.x in the code base which generate difficult errors to debug.I'm in Ch 3 and scrapping the book for something else. Sad.Update:I have seen improvements in Ch4 with CNN's the code is cleaner and the explanation is improving.
Amazon Verified review Amazon
Barry Hart Jun 30, 2017
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Much of the book seems like a rehash or even direct quotes from the TensorFlow Dev Summit put on by Google in early 2017. If you've watched the videos, this book might serve as a good set of notes. The book doesn't have much flow or unity. If you're looking to learn TensorFlow from scratch, I recommend Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems instead.
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Dimitri Shvorob Mar 12, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
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. One week later, I am still not done, but have winnowed out some options. The books by Van Boxel and Zaccone are out - these are brief (and not up-to-date) introductions, costing as much as more substantial titles - and the book by Karim is "disqualified" for blatant plagiarism.What happens when Messrs Zaccone and Karim collaborate? It seems that the former continues to do honest work, and the latter continues to pilfer content. I am reading an electronic copy and cannot point to page numbers, but a chunk of "Neural networks as computational graphs" Section in Chapter 1 seems to be purloined from Brandon Brown of Outlace.com (see post "On chain rule, computational graphs, and back-propagation"), and Chapter 10, "Reinforcement learning", is taken without attribution from Arthur Juliani. Sorry, I have to stop here, give one star and move on. It's a pity, because "Deep Learning with TensorFlow" has substance - as copy-pastes go, this is a fairly wide-ranging, and undeniably enriched, copy-paste - but plagiarism should not be encouraged.
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