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 been introduced to DRL. A powerful technique believed by many researchers as the most promising lead towards artificial intelligence. Together, we've gone over the principles of RL. RL is able to solve many toy problems, but the Q-Table is unable to scale to more complex real-world problems. The solution is to learn the Q-Table using a deep neural network. However, training deep neural networks on RL is highly unstable due to sample correlation and non-stationarity of the target Q-Network.

DQN proposed a solution to these problems using experience replay and separating the target network from the Q-Network under training. DDQN suggested further improvement of the algorithm by separating the action selection from action evaluation to minimize the overestimation of Q value. There are other improvements proposed for the DQN. Prioritized experience replay [6] argues that that experience buffer should not be sampled uniformly. Instead, experiences that...