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Sinopsis
Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required.
Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.
We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment.
• Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence
Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.
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Acerca del autor
Miguel Morales is a Senior Software Engineer at Lockheed Martin, Missile and Fire Control-Autonomous Systems. He is also a faculty member at Georgia Institute of Technology where he works as an Instructional Associate for the Reinforcement Learning and Decision Making graduate course. Miguel has worked for numerous other educational and technology companies including Udacity, AT&T, Cisco, and HPE.
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Grokking Deep Reinforcement Learning
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GROKKING DEEP REINFORCEMENT LEARNING
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Grokking Deep Reinforcement Learning
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Grokking Deep Reinforcement Learning
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Grokking Deep Reinforcement Learning
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Grokking Deep Reinforcement Learning
Publicado por
Manning Publications, US, 2021
ISBN 10: 1617295450
ISBN 13: 9781617295454
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Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. . Foundational reinforcement learning concepts and methods . The most popular deep reinforcement learning agents solving high-dimensional environments . Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return. Nº de ref. del artículo: LU-9781617295454
Cantidad disponible: 1 disponibles
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Grokking Deep Reinforcement Learning (DRL)
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Manning Pubns Co, Shelter Island, 2020
ISBN 10: 1617295450
ISBN 13: 9781617295454
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Grokking Deep Reinforcement Learning
Publicado por
Manning Publications, 2021
ISBN 10: 1617295450
ISBN 13: 9781617295454
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Condición: New. 2020. 1st Edition. Paperback. . . . . . Nº de ref. del artículo: V9781617295454
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Grokking Deep Reinforcement Learning
Publicado por
Manning Publications, US, 2021
ISBN 10: 1617295450
ISBN 13: 9781617295454
Nuevo
Paperback
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. . Foundational reinforcement learning concepts and methods . The most popular deep reinforcement learning agents solving high-dimensional environments . Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return. Nº de ref. del artículo: LU-9781617295454
Cantidad disponible: 1 disponibles
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Grokking Deep Reinforcement Learning
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