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)


In the same spirit as InfoGAN, StackedGAN proposes a method for disentangling latent representations for conditioning generator outputs. However, StackedGAN uses a different approach to the problem. Instead of learning how to condition the noise to produce the desired output, StackedGAN breaks down a GAN into a stack of GANs. Each GAN is trained independently in the usual discriminator-adversarial manner with its own latent code.

Figure 6.2.1 shows us how StackedGAN works in the context of the hypothetical celebrity face generation. Assuming that the Encoder network is trained to classify celebrity faces.

The Encoder network is made of a stack of simple encoders, Encoder i where i = 0 … n - 1 corresponding to n features. Each encoder extracts certain facial features. For example, Encoder0 may be the encoder for hairstyle features, Features1. All the simple encoders contribute to making the overall Encoder perform correct predictions.

The idea behind StackedGAN is that if we would...