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

Temporal-difference learning

Q-Learning is a special case of a more generalized Temporal-Difference Learning or TD-Learning Temporal-difference learning. More specifically, it's a special case of one-step TD-Learning TD(0):

Temporal-difference learning (Equation 9.5.1)

In the equation Temporal-difference learning is the learning rate. We should note that when Temporal-difference learning, Equation 9.5.1 is similar to the Bellman equation. For simplicity, we'll refer to Equation 9.5.1 as Q-Learning or generalized Q-Learning.

Previously, we referred to Q-Learning as an off-policy RL algorithm since it learns the Q value function without directly using the policy that it is trying to optimize. An example of an on-policy one-step TD-learning algorithm is SARSA which similar to Equation 9.5.1:

Temporal-difference learning (Equation 9.5.2)

The main difference is the use of the policy that is being optimized to determine a'. The terms s, a, r, s' and a' (thus the name SARSA) must be known to update the Q value function at every iteration. Both Q-Learning and SARSA use existing estimates...