Book Image

Python Deep Learning - Second Edition

By : Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Book Image

Python Deep Learning - Second Edition

By: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
Table of Contents (12 chapters)

RL paradigms

In this section, we'll talk about the main paradigms of RL. We first mentioned some of them in Chapter 1, Machine Learning: an Introduction, but it's worth discussing them here to refresh our memory and for the sake of completeness. To help us with this task, we'll use a maze game as an example. The maze is represented by a rectangular grid, where grid cells with a value of 0 represent the walls, and the cells with a value of 1 are the paths. Some locations contain intermediate rewards. An agent in the maze can use the paths to move between locations. Its objective is to navigate its way to the other end of the maze and to get the largest possible reward while doing so. The following is a diagram describing the basic principles of how RL works:

Reinforcement learning scenario

Here are some elements of an RL system:

  • Agent: The entity for which we are...