As we discussed in the Value function approximation section, we are training the network online, as the agent receives stream of experiences from the environment. But the environment is usually sequential, and consecutive experiences might not differ much. For example, imagine that the agent is a car, which is currently sliding downhill. While doing so, it receives consistent feedback that the speed increases. If we feed the network with such unified training data, there is a chance that it will start dominating all other experiences. The network might "forget" previous situations and the performance would decrease (this is a disadvantage of some neural networks). We can solve this issue with experience replay. As the environment interaction goes, we'll store a sliding window of the latest n interactions: (state st-1, action at-1, reward rt, state...
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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