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)

Monte Carlo methods

In this section, we'll describe our first algorithm, which does not require full knowledge of the environment (model-free): the Monte Carlo (MC) method (yay, I guess...). Here, the agent uses its own experience to find the optimal policy.

Policy evaluation

In the Dynamic programming section, we'll describe how to estimate the value function, , given a policy, π (planning). MC does this by playing full episodes, and then averaging the cumulative returns for each state over the different episodes.

Let's see how it works in the following steps:

  1. Input the policy, π.
  2. Initialize the following:
    • Thetable with some value for all states
    • An empty list of returns(s) for each state, s