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)

Q-learning in action

In this section, we'll use Q-learning in combination with a simple neural network to control an agent in the cart-pole task. We'll use an ε-greedy policy and experience replay. This is a classic RL problem. The agent must balance a pole attached to the cart via a joint. At every step, the agent can move the cart left or right. It receives a reward of 1 every time step that the pole is balanced. If the pole deviates by more than 15 degrees from upright, the game ends:

The cart-pole task

To help us with this, we'll use OpenAI Gym (, which is an open source toolkit for the development and comparison of RL algorithms. It allows us to teach agents over various tasks, such as walking or playing games such as Pong, Pinball, other Atari games, and even Doom.

We can install it with pip:

    pip install gym[all]

Next, let...