Book Image

Keras 2.x Projects

By : Giuseppe Ciaburro
Book Image

Keras 2.x Projects

By: Giuseppe Ciaburro

Overview of this book

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Table of Contents (13 chapters)

Summary

In this chapter, we learned about the basic concepts of reinforcement learning and how to use these techniques to control a mechanical system. To start with, an overview of robot control was addressed.

Then, the OpenAI Gym library was introduced, which helps us to implement algorithms based on reinforcement learning. It includes a growing collection of benchmark issues that expose a common interface, and a website where people can share their results and compare algorithm performance. We explored the different environments that are available and how to install the library.

Finally, the CartPole system was used to implement Q-learning and Deep Q-learning algorithms. The CartPole system is a classic problem of reinforced learning. The system consists of a pole (which acts like an inverted pendulum) that's attached to a cart via a joint. The system is controlled by applying...