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)

Q-Learning in Python

The environment and the Q-Learning discussed in the previous section can be implemented in Python. Since the policy is just a simple table, there is, at this point in time no need for Keras. Listing 9.3.1 shows, the implementation of the simple deterministic world (environment, agent, action, and Q-Table algorithms) using the QWorld class. For conciseness, the functions dealing with the user interface are not shown.

In this example, the environment dynamics is represented by self.transition_table. At every action, self.transition_table determines the next state. The reward for executing an action is stored in self.reward_table. The two tables are consulted every time an action is executed by the step() function. The Q-Learning algorithm is implemented by update_q_table() function. Every time the agent needs to decide which action to take, it calls the act() function. The action may be randomly drawn or decided by the policy using the Q-Table...