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The Deep Learning Workshop

You're reading from   The Deep Learning Workshop Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839219856
Length 474 pages
Edition 1st Edition
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Authors (5):
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Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mohan Kumar Silaparasetty Mohan Kumar Silaparasetty
Author Profile Icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
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Toc

Table of Contents (9) Chapters Close

Preface
1. Building Blocks of Deep Learning 2. Neural Networks FREE CHAPTER 3. Image Classification with Convolutional Neural Networks (CNNs) 4. Deep Learning for Text – Embeddings 5. Deep Learning for Sequences 6. LSTMs, GRUs, and Advanced RNNs 7. Generative Adversarial Networks Appendix

Bidirectional RNNs

The RNN models we've just looked at – LSTMs, GRUs – are powerful indeed and provide extremely good results when it comes to sequence-processing tasks. Now, let's discuss how to make them even more powerful, and the methods that yield the amazing successes in deep learning that you have been hearing about.

Let's begin with the idea of bidirectional RNNs. The idea applies to all variants of RNNs, including, but not limited to, LSTMs and GRUs. Bidirectional RNNs process the sequence in both directions, allowing the network to have both backward and forward information about the sequence, providing it with a much richer context:

Figure 6.17: Bidirectional LSTM

The bidirectional model essentially employs two RNNs in parallel – one as the "forward layer" and the other as the "backward layer". As shown in the preceding figure, the forward layer processes the sequence in the order of its...

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