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Deep Learning with TensorFlow
Deep Learning with TensorFlow

Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with Python , Second Edition

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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
  • • Gain real-world contextualization through some deep learning problems concerning research and application

Description

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.

Who is this book for?

The book is for 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

  • • Apply deep machine intelligence and GPU computing with TensorFlow
  • • Access public datasets and use TensorFlow to load, process, and transform the data
  • • Discover how to use the high-level TensorFlow API to build more powerful applications
  • • Use 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 30, 2018
Length: 484 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788831833
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What do you get with eBook?

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Product Details

Publication date : Mar 30, 2018
Length: 484 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788831833
Category :
Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
1. Getting Started with Deep Learning Chevron down icon Chevron up icon
2. A First Look at TensorFlow Chevron down icon Chevron up icon
3. Feed-Forward Neural Networks with TensorFlow Chevron down icon Chevron up icon
4. Convolutional Neural Networks Chevron down icon Chevron up icon
5. Optimizing TensorFlow Autoencoders Chevron down icon Chevron up icon
6. Recurrent Neural Networks Chevron down icon Chevron up icon
7. Heterogeneous and Distributed Computing Chevron down icon Chevron up icon
8. Advanced TensorFlow Programming Chevron down icon Chevron up icon
9. Recommendation Systems Using Factorization Machines Chevron down icon Chevron up icon
10. Reinforcement Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 25%
4 star 0%
3 star 50%
2 star 0%
1 star 25%
Wilfrido felson Salgado Bermudez Jun 12, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It is an excellent book, maybe a little complicated at first because is a new topic for me. You can learn in a structured way.
Amazon Verified review Amazon
ten May 12, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
authors don't explain much what they do and why they do it. It would have been a great book if they explained to some extent their codes. They treated this book as a code repository of some projects they worked on. they talk about the concept briefly then throw a bunch of codes. During code reviews , you need to explain your codes. This is part is missing.
Amazon Verified review Amazon
CyberExp Jan 05, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
The Good: (1) Tensorflow overview is nicely collected. (2) Use native Tensorflow and not just keras. Only OK: The rest of the book shows code that the authors had generated, with a little more comments than what a Github entry would provide. If you already understand neural networks such as CNN and collaborative filtering, it might be OK to look at some code. The explanation of the code is hastily and scarcely written.
Amazon Verified review Amazon
RM May 04, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Terrible. The authors are apparently not very comfortable in the English language. A technical book should not have ambiguous/contradictory statements.
Amazon Verified review Amazon
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