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Deep Learning with TensorFlow and Keras – 3rd edition

You're reading from   Deep Learning with TensorFlow and Keras – 3rd edition Build and deploy supervised, unsupervised, deep, and reinforcement learning models

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803232911
Length 698 pages
Edition 3rd Edition
Tools
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Authors (3):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Toc

Table of Contents (23) Chapters Close

Preface 1. Neural Network Foundations with TF 2. Regression and Classification FREE CHAPTER 3. Convolutional Neural Networks 4. Word Embeddings 5. Recurrent Neural Networks 6. Transformers 7. Unsupervised Learning 8. Autoencoders 9. Generative Models 10. Self-Supervised Learning 11. Reinforcement Learning 12. Probabilistic TensorFlow 13. An Introduction to AutoML 14. The Math Behind Deep Learning 15. Tensor Processing Unit 16. Other Useful Deep Learning Libraries 17. Graph Neural Networks 18. Machine Learning Best Practices 19. TensorFlow 2 Ecosystem 20. Advanced Convolutional Neural Networks 21. Other Books You May Enjoy
22. Index

Summary

In this chapter, we learned about RNNs, a class of networks that is specialized for dealing with sequences such as natural language, time series, speech, and so on. Just like CNNs exploit the geometry of images, RNNs exploit the sequential structure of their inputs. We learned about the basic RNN cell, how it handles state from previous time steps, and how it suffers from vanishing and exploding gradients because of inherent problems with BPTT. We saw how these problems lead to the development of novel RNN cell architectures such as LSTM, GRU, and peephole LSTMs. We also learned about some simple ways to make your RNN more effective, such as making it bidirectional or stateful.

We then looked at different RNN topologies and how each topology is adapted to a particular set of problems. After a lot of theory, we finally saw examples of three of these topologies. We then focused on one of these topologies, called seq2seq, which first gained popularity in the machine translation...

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