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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

Summarizing All the Models

In this chapter, we've looked at different variants of RNNs – from plain RNNs to LSTMs to GRUs. We also looked at the bidirectional approach and the stacking approach to using RNNs. Now is a good time to take a holistic look at things and make a comparison between the models. Let's look at the following table, which compares the five models in terms of parameters, training time, and performance (that is, the level of accuracy on our dataset):

Figure 6.23: Comparing the five models

Note

As mentioned earlier in the chapter, while working through the practical elements, you may have obtained values different from the ones shown above; however, the test accuracies you obtain should largely agree with ours. If the model's performance is very different, you may want to tweak the number of epochs.

Plain RNNs are the lowest on parameters and have the lowest training times but have the lowest accuracy of all the...

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