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

Chapter 10. Policy Gradient Methods

In the final chapter of this book, we're going to introduce algorithms that directly optimize the policy network in reinforcement learning. These algorithms are collectively referred to as policy gradient methods. Since the policy network is directly optimized during training, the policy gradient methods belong to the family of on-policy reinforcement learning algorithms. Like value-based methods that we discussed in Chapter 9, Deep Reinforcement Learning, policy gradient methods can also be implemented as deep reinforcement learning algorithms.

A fundamental motivation in studying the policy gradient methods is addressing the limitations of Q-Learning. We'll recall that Q-Learning is about selecting the action that maximizes the value of the state. With Q function, we're able to determine the policy that enables the agent to decide on which action to take for a given state. The chosen...