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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

Implementation

In this section, we will go through a few tasks using transformers.

Transformer reference implementation: An example of translation

In this section, we will briefly review a transformer reference implementation available at https://www.tensorflow.org/text/tutorials/transformer and specifically, we will use the opportunity to run the code in a Google Colab.

Not everyone realizes the number of GPUs it takes to train a transformer. Luckily, you can play with resources available for free at https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/transformer.ipynb.

Note that implementing transformers from scratch is probably not the best choice unless you need to realize some very specific customization or you are interested in core research. If you are not interested in learning the internals, then you can skip to the next section. Our tutorial is licensed under the Creative Commons Attribution 4.0 License, and code samples are...

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