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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

GPGPU history

The general purpose computing on graphics processing unit (GPGPU) recognizes the trend to employ GPU technology for non-graphic applications. Until 2006, the graphics API OpenGL and DirectX standards were the only ways to program with the GPU. Any attempt to execute arbitrary calculations on the GPU was subject to the programming restrictions of those APIs.

The GPUs were designed to produce a color for each pixel on the screen using programmable arithmetic units called pixel shaders. The programmers realized that if the inputs were numerical data, with a different meaning from the pixel colors, then they could program the pixel shader to perform arbitrary computations.

The GPU was deceived by showing general tasks such as rendering tasks; this deception was intelligent, but also very convoluted.

There were memory limitations because the programs could only receive a handful of input color and texture...

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