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Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;
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Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required;
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Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key;
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This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;
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Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve;
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cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along;
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A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the;
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deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;
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deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to;
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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL;
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Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key Features Implement Q-learning;
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Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;
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Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important;
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Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as;
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(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;
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(CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud;
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Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more Build deep learning workflows and take deep;
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Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features Apply the;
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New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and;
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Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key Features Your entry;
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Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and;
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, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing;
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convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic;
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reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q;
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This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;
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This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory;
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