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

Q-Learning on OpenAI gym

Before presenting another example, there appears to be a need for a suitable RL simulation environment. Otherwise, we can only run RL simulations on very simple problems like in the previous example. Fortunately, OpenAI created Gym, https://gym.openai.com.

The gym is a toolkit for developing and comparing RL algorithms. It works with most deep learning libraries, including Keras. The gym can be installed by running the following command:

$ sudo pip3 install gym

The gym has several environments where an RL algorithm can be tested against such as toy text, classic control, algorithmic, Atari, and 2D/3D robots. For example, FrozenLake-v0 (Figure 9.5.1) is a toy text environment similar to the simple deterministic world used in the Q-Learning in Python example. FrozenLake-v0 has 12 states. The state marked S is the starting state, F is the frozen part of the lake which is safe, H is the Hole state that should be avoided, and G is the Goal state where the frisbee...