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 this chapter, we've presented various improvements in the original algorithm of GAN, first introduced in the previous chapter. WGAN proposed an algorithm to improve the stability of training by using the EMD or Wassertein 1 loss. LSGAN argued that the original cross-entropy function of GAN is prone to vanishing gradients, unlike least squares loss. LSGAN proposed an algorithm to achieve stable training and quality outputs. ACGAN convincingly improved the quality of the conditional generation of MNIST digits by requiring the discriminator to perform classification task on top of determining whether the input image is fake or real.

In the next chapter, we'll study how to control the attributes of generator outputs. Whilst CGAN and ACGAN are able to indicate the desired digits to produce; we have not analyzed GANs that can specify the attributes of outputs. For example, we may want to control the writing style of the MNIST digits such as roundness, tilt angle, and thickness. Therefore...