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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning 2. First Look at TensorFlow FREE CHAPTER 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Accelerated Linear Algebra

The Accelerated Linear Algebra (XLA) is a domain specific compiler developed by TensorFlow for optimizing its computations. By this, you will get improvements in speed, memory usage and even portability on mobile platforms.

Initially, you won't see much benefit from XLA because it's still experimental but you can try it by using the just-in-time compilation or ahead-of-time compilations.

First, we are going to briefly mention the key strengths of TensorFlow and see how TensorFlow team had the challenge to keep and increase these key strengths.

Key strengths of TensorFlow

The following are the key strengths of TensorFlow:

  • Flexible: This Flexibility is coming from TensorFlow's nature of being interpreted. Also, you can see...
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