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Deep Learning with PyTorch Quick Start Guide

You're reading from  Deep Learning with PyTorch Quick Start Guide

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781789534092
Pages 158 pages
Edition 1st Edition
Languages
Author (1):
David Julian David Julian
Profile icon David Julian

Convolutional Networks

Previously, we built several simple networks to solve regression and classification problems. These illustrated the basic code structure and concepts involved in building ANNs with PyTorch.

In this chapter, we will extend simple linear models by adding layers and using convolutional layers to solve nonlinear problems found in real-world examples. Specifically, we will cover the following topics:

  • Hyper-parameters and multilayered networks
  • Build a simple benchmarking function to train and test models
  • Convolutional networks
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