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Hands-On Mathematics for Deep Learning

You're reading from   Hands-On Mathematics for Deep Learning Build a solid mathematical foundation for training efficient deep neural networks

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Length 364 pages
Edition 1st Edition
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Author (1):
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Jay Dawani Jay Dawani
Author Profile Icon Jay Dawani
Jay Dawani
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra FREE CHAPTER 3. Vector Calculus 4. Probability and Statistics 5. Optimization 6. Graph Theory 7. Section 2: Essential Neural Networks
8. Linear Neural Networks 9. Feedforward Neural Networks 10. Regularization 11. Convolutional Neural Networks 12. Recurrent Neural Networks 13. Section 3: Advanced Deep Learning Concepts Simplified
14. Attention Mechanisms 15. Generative Models 16. Transfer and Meta Learning 17. Geometric Deep Learning 18. Other Books You May Enjoy

Popular architecture

Now that we have learned about all the components that are used to contrast RNNs, let's explore a popular architecture that has been developed by researchers in the field—the clockwork RNN (CW-RNN).

Clockwork RNNs

As we have learned, it is very challenging to discover long-term dependencies in RNNs, and LSTMs and GRUs were designed to overcome this limitation. CW-RNN, created by a group at IDSIA led by Jürgen Schmidhuber, modifies the vanilla RNN module such that the hidden layer is partitioned into separate modules, each of which processes its inputs at different temporal granularities. What this means is that the hidden layers perform computations at their preset clock rate (which is...

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