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

Automatic colorization autoencoder


We're now going to work on another practical application of autoencoders. In this case, we're going to imagine that we have a grayscale photo and that we want to build a tool that will automatically add color to them. We would like to replicate the human abilities in identifying that the sea and sky are blue, the grass field and trees are green, while clouds are white, and so on.

As shown in Figure 3.4.1, if we are given a grayscale photo of a rice field on the foreground, a volcano in the background and sky on top, we're able to add the appropriate colors.

Figure 3.4.1: Adding color to a grayscale photo of the Mayon Volcano. A colorization network should replicate human abilities by adding color to a grayscale photo. Left photo is grayscale. The right photo is color. Original color photo can be found on the book GitHub repository, https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter3-autoencoders/README.md.

A simple automatic...