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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

The chapter covered the major unsupervised learning algorithms. We went through algorithms best suited for dimension reduction, clustering, and image reconstruction. We started with the dimension reduction algorithm PCA, then we performed clustering using k-means and self-organized maps. After this we studied the restricted Boltzmann machine and saw how we can use it for both dimension reduction and image reconstruction. Next the chapter delved into stacked RBMs, that is, deep belief networks, and we trained a DBN consisting of three RBM layers on the MNIST dataset. Lastly, we learned about variational autoencoders, which, like GANs, can generate images after learning the distribution of the input sample space.

This chapter, along with chapters 6 and 9, covered models that were trained using unsupervised learning. In the next chapter, we move on to another learning paradigm: reinforcement learning.

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Deep Learning with TensorFlow 2 and Keras
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