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 covered the policy gradient methods. Starting with the policy gradient theorem, we formulated four methods to train the policy network. The four methods, REINFORCE, REINFORCE with baseline, Actor-Critic, and A2C algorithms were discussed in detail. We explored how the four methods could be implemented in Keras. We then validated the algorithms by examining the number of times the agent successfully reached its goal and in terms of the total rewards received per episode.

Similar to Deep Q-Network [3] that we discussed in the previous chapter, there are several improvements that can be done on the fundamental policy gradient algorithms. For example, the most prominent one is the A3C [4] which is a multi-threaded version of A2C. This enables the agent to get exposed to different experiences simultaneously and to optimize the policy and value networks asynchronously. However, in the experiments conducted by OpenAI,