We ended Chapter 8, Reinforcement Learning Theory, with an example of an agent learning to play the cart-pole game with the help of Q-learning and a simple network with one hidden layer. The state of the cart-pole environment is described with four numerical variables: cart position and velocity, and pole angle and velocity. We used these variables as an input to the q-function approximation network and successfully trained the agent to prevent the pole from tipping over for more than 200 episode steps. But if it was a human playing the game, he or she would steer the cart based on the screen images he or she sees. That is, if we think of the human as an "agent," the environment "state" he or she would use would be the sequence of frames displayed on the screen. Compare this to just four variables our artificial agent used, and you'll see...
Python Deep Learning - Second Edition
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Python Deep Learning - Second Edition
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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.
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Python Deep Learning - Second Edition
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Table of Contents (12 chapters)
Preface
Free Chapter
Machine Learning - an Introduction
Neural Networks
Deep Learning Fundamentals
Computer Vision with Convolutional Networks
Advanced Computer Vision
Generating Images with GANs and VAEs
Recurrent Neural Networks and Language Models
Reinforcement Learning Theory
Deep Reinforcement Learning for Games
Deep Learning in Autonomous Vehicles
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