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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Dr. Amita Kapoor, Sujit Pal
4.3 (26)
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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

4.3 (26)
By: Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

Summary

This chapter explored one of the most exciting deep neural networks of our times: GANs. Unlike discriminative networks, GANs have an ability to generate images based on the probability distribution of the input space. We started with the first GAN model proposed by Ian Goodfellow and used it to generate handwritten digits. We next moved to DCGANs where convolutional neural networks were used to generate images and we saw the remarkable pictures of celebrities, bedrooms, and even album artwork generated by DCGANs. Finally, the chapter delved into some awesome GAN architectures: the SRGAN, CycleGAN, and InfoGAN. The chapter also included an implementation of the CycleGAN in TensorFlow 2.0.

In this chapter and the ones before it we have been largely concerned with images; the next chapter will move into textual data. You will learn about word embeddings and learn to use some of the recent pretrained language models for embeddings.

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Tech Concepts
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Deep Learning with TensorFlow 2 and Keras
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