Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (16 chapters)
14
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15
Index

2. Mutual Information and Entropy

MI can also be interpreted in terms of entropy. Recall from Chapter 6, Disentangled Representation GANs, that entropy, H(X), is a measure of the expected amount of information of a random variable X:

(Equation 13.2.1)

Equation 13.2.1 implies that entropy is also a measure of uncertainty. The occurrence of uncertain events gives us a higher amount of surprise, or information. For example, news about an employee's unexpected promotion has a high amount of information, or entropy.

Using Equation 13.2.1, MI can be expressed as:

(Equation 13.2.2)

Equation 13.2.2 implies that MI increases with marginal entropy but decreases with joint entropy. A more common expression for MI in terms of entropy is as follows:

(Equation 13.2.3)

Equation 13.2.3 tells us that MI increases with the entropy of a random variable but decreases with the conditional entropy on another random variable. Alternatively...