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)


This chapter discussed the general principles behind GANs, to give you a foundation to the more advanced topics we'll now move on to, including Improved GANs, Disentangled Representations GANs, and Cross-Doman GANs. We started this chapter by understanding how GANs are made up of two networks called generator and discriminator. The role of the discriminator is to discriminate between real and fake signals. The aim of the generator is to fool the discriminator. The generator is normally combined with the discriminator to form an adversarial network. It is through training the adversarial network that the generator learns how to produce fake signals that can trick the discriminator.

We also learned how GANs are easy to build but notoriously difficult to train. Two example implementations in Keras were presented. DCGAN demonstrated that it is possible to train GANs to generate fake images using deep CNNs. The fake images are MNIST digits. However, the DCGAN generator has no control...