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Neural Networks with R

You're reading from  Neural Networks with R

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Pages 270 pages
Edition 1st Edition
Languages
Authors (2):
Balaji Venkateswaran Balaji Venkateswaran
Profile icon Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
View More author details
Toc

Table of Contents (14) Chapters close

Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Quick note on GPU processing


The increase in processing capabilities has been a tremendous booster for usage of neural networks in day-to-day problems. GPU is a specialized processor designed to perform graphical operations (for example, gaming, 3D animation, and so on). They perform mathematically intensive tasks and are additional to the CPU. The CPU performs the operational tasks of the computer, while the GPU is used to perform heavy workload processing.

The neural network architecture needs heavy mathematical computational capabilities and GPU is the preferred candidate here. The vectorized dot matrix product between the weights and inputs at every neuron can be run in parallel through GPUs. The advancements in GPUs is popularizing neural networks. The applications of DL in image processing, computer vision, bioinformatics, and weather modeling are benefiting through GPUs.

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Neural Networks with R
Published in: Sep 2017 Publisher: Packt ISBN-13: 9781788397872
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