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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 FREE CHAPTER 2. First Look at TensorFlow 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

GPU programming model

At this point it is necessary to introduce some basic concepts to understand the CUDA programming model. The first distinction is between host and device.

The code executed in the host side is the part of code executed on the CPU, and this will also include the RAM and the hard disk.

However, the code executed on the device is automatically loaded on the graphic card and run on the latter. Another important concept is the kernel; it stands for a function performed on the device and launched from the host.

The code defined in the kernel will be performed in parallel by an array of threads. The following figure summarizes how the GPU programming model works:

  • The running program will have source code to run on CPU and code to run on GPU
  • CPU and GPU have separated memories
  • The data is transferred from CPU to GPU to be computed
  • The data output from GPU computation is copied back to CPU memory
...
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