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Deep Learning with PyTorch

You're reading from   Deep Learning with PyTorch A practical approach to building neural network models using PyTorch

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788624336
Length 262 pages
Edition 1st Edition
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Author (1):
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Vishnu Subramanian Vishnu Subramanian
Author Profile Icon Vishnu Subramanian
Vishnu Subramanian
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning Using PyTorch 2. Building Blocks of Neural Networks FREE CHAPTER 3. Diving Deep into Neural Networks 4. Fundamentals of Machine Learning 5. Deep Learning for Computer Vision 6. Deep Learning with Sequence Data and Text 7. Generative Networks 8. Modern Network Architectures 9. What Next? 10. Other Books You May Enjoy

Convolutional network on sequence data

We learned how CNNs solve problems in computer vision by learning features from the images. In images, CNNs work by convolving across height and width. In the same way, time can be treated as a convolutional feature. One-dimensional convolutions sometimes perform better than RNNs and are computationally cheaper. In the last few years, companies like Facebook have shown success in audio generation and machine translation. In this section, we will learn how CNNs can be used to build a text classification solution.

Understanding one-dimensional convolution for sequence data

In Chapter 5, Deep Learning for Computer Vision, we have seen how two-dimensional weights are learned from the training...

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