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Deep Learning with TensorFlow

You're reading from  Deep Learning with TensorFlow

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Pages 320 pages
Edition 1st Edition
Languages
Authors (3):
Giancarlo Zaccone Giancarlo Zaccone
Profile icon Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Profile icon Ahmed Menshawy
View More author details

Table of Contents (11) Chapters

Preface Getting Started with Deep Learning First Look at TensorFlow Using TensorFlow on a Feed-Forward Neural Network TensorFlow on a Convolutional Neural Network Optimizing TensorFlow Autoencoders Recurrent Neural Networks GPU Computing Advanced TensorFlow Programming Advanced Multimedia Programming with TensorFlow 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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