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)

Generator outputs of CycleGAN

Figure 7.1.9 shows the colorization results of CycleGAN. The source images are from the test dataset. For comparison, we show the ground truth and the colorization results using a plain autoencoder described in Chapter 3, Autoencoders. Generally, all colorized images are perceptually acceptable. Overall, it seems that each colorization technique has both its own pros and cons. All colorization methods are not consistent with the right color of the sky and vehicle.

For example, the sky in the background of the plane (3rd row, 2nd column) is white. The autoencoder got it right, but the CycleGAN thinks it is light brown or blue. For the 6th row, 6th column, the boat on the dark sea had an overcast sky but was colorized with blue sky and blue sea by autoencoder and blue sea and white sky by CycleGAN without PatchGAN. Both predictions make sense in the real world. Meanwhile, the prediction of CycleGAN with PatchGAN is similar to the ground truth. On 2nd to...