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The TensorFlow Workshop

You're reading from   The TensorFlow Workshop A hands-on guide to building deep learning models from scratch using real-world datasets

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800205253
Length 600 pages
Edition 1st Edition
Languages
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Authors (4):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Abhranshu Bagchi Abhranshu Bagchi
Author Profile Icon Abhranshu Bagchi
Abhranshu Bagchi
Anthony Maddalone Anthony Maddalone
Author Profile Icon Anthony Maddalone
Anthony Maddalone
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
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Toc

Table of Contents (13) Chapters Close

Preface
1. Introduction to Machine Learning with TensorFlow 2. Loading and Processing Data FREE CHAPTER 3. TensorFlow Development 4. Regression and Classification Models 5. Classification Models 6. Regularization and Hyperparameter Tuning 7. Convolutional Neural Networks 8. Pre-Trained Networks 9. Recurrent Neural Networks 10. Custom TensorFlow Components 11. Generative Models Appendix

Summary

In this chapter, you learned about a very exciting class of machine learning models called generative models. You discovered the amazing potential of this new and continually developing field in machine learning by using a generative LSTM on a language modeling challenge to generate textual output.

Then, you learned about generative adversarial models. You implemented a GAN to generate data for a normal distribution of points. You also went even further into deep convolutional neural networks (DCGANS), discovering how to use one of the most powerful applications of GANs while creating new images of tomatoes and bananas that exhibited human-recognizable characteristics of the fruits on which they were trained.

We hope you enjoyed the final chapter of The TensorFlow Workshop and the book as a whole.

Let's take a look back at the amazing journey that you have completed. First, you started by learning the basics of TensorFlow and how to perform operations on...

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