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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

LSTM

RNNs are quite popular in building real-world applications like language translation, text classification and many more sequential problems, but in reality, we rarely would use a vanilla version of RNN which we saw in the previous section. The vanilla version of RNN has problems like vanishing gradients and gradient explosion when dealing with large sequences. In most of the real-world problems, variants of RNN such as LSTM or GRU are used, which solve the limitations of plain RNN and also have the ability to handle sequential data better. We will try to understand what happens in LSTM, and build a network based on LSTM to solve the text classification problem on the IMDB datasets.

Long-term dependency

RNNs, in theory...

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