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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

The CUDA architecture

In 2006, NVIDIA was presented as the first GPU to support DirectX 10; the GeForce 8800GTX was also the first GPU to use the CUDA architecture. This architecture included several new components designed specifically for GPU computing and aimed to remove the limitations that prevented them that previous GPUs were used for non-graphical calculations. In fact, the execution units on the GPU could read and write arbitrary memory as well as access a cache maintained in software called shared memory. These architectural features were added to make a CUDA GPU that also excelled in general purpose calculations as well as in traditional graphics tasks.

The following figure summarizes the division of space between the various components of a graphics processing unit (GPU) and a central processing unit (CPU). As you can see, a GPU devotes more transistors to data processing; it is a highly parallel, multithreaded...

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