Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)
12
Index

Conditional VAE (CVAE)-VAE: VAE with disentangled latent representations

In Chapter 6, Disentangled Representation GANs, the concept, and importance of the disentangled representation of latent codes were discussed. We can recall that a disentangled representation is where single latent units are sensitive to changes in single generative factors while being relatively invariant to changes in other factors [3]. Varying a latent code results to changes in one attribute of the generated output while the rest of the properties remain the same.

In the same chapter, InfoGANs [4] demonstrated to us that in the MNIST dataset, it is possible to control which digit to generate and the tilt and thickness of writing style. Observing the results in the previous section, it can be noticed that the VAE is intrinsically disentangling the latent vector dimensions to a certain extent. For example, looking at digit 8 in Figure 8.2.6, navigating z[1] from top to bottom decreases the width and roundness...