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)

Policy gradient theorem

As discussed in Chapter 9, Deep Reinforcement Learning, in Reinforcement Learning the agent is situated in an environment that is in state st', an element of state space Policy gradient theorem. The state space Policy gradient theorem may be discrete or continuous. The agent takes an action at from the action space Policy gradient theorem by obeying the policy, Policy gradient theorem. Policy gradient theorem may be discrete or continuous. Because of executing the action at, the agent receives a reward r t+1 and the environment transitions to a new state s t+1. The new state is dependent only on the current state and action. The goal of the agent is to learn an optimal policy Policy gradient theorem that maximizes the return from all the states:

Policy gradient theorem (Equation 9.1.1)

The return, Policy gradient theorem, is defined as the discounted cumulative reward from time t until the end of the episode or when the terminal state is reached:

Policy gradient theorem (Equation 9.1.2)

From Equation 9.1.2, the return can also be interpreted as a value of a given state by following the policy Policy gradient theorem. It can be observed from Equation 9.1.1 that future...