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

Chapter 8. Variational Autoencoders (VAEs)

Similar to Generative Adversarial Networks (GANs) that we've discussed in the previous chapters, Variational Autoencoders (VAEs) [1] belong to the family of generative models. The generator of VAE is able to produce meaningful outputs while navigating its continuous latent space. The possible attributes of the decoder outputs are explored through the latent vector.

In GANs, the focus is on how to arrive at a model that approximates the input distribution. VAEs attempt to model the input distribution from a decodable continuous latent space. This is one of the possible underlying reasons why GANs are able to generate more realistic signals when compared to VAEs. For example, in image generation, GANs are able to produce more realistic looking images while VAEs in comparison generate images that are less sharp.

Within VAEs, the focus is on the variational inference of latent codes. Therefore, VAEs provide a suitable framework...