Author:
Publisher:
ISBN: 9780913251409
Category : Artificial intelligence
Languages : en
Pages :

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Book Description

Author:
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 360

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Book Description

Author:
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 424

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Book Description

Author: UNESCO International Centre for Technical and Vocational Education and Training
Publisher: UNESCO Publishing
ISBN: 9231004468
Category : Political Science
Languages : en
Pages : 56

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Author:
Publisher:
ISBN:
Category : Industrial engineering
Languages : en
Pages : 168

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Book Description

Author: United States. National Aeronautics and Space Administration
Publisher:
ISBN:
Category :
Languages : en
Pages : 752

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Author:
Publisher:
ISBN:
Category : Industrial engineering
Languages : en
Pages : 736

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Book Description

Author:
Publisher:
ISBN: 9780913251409
Category : Artificial intelligence
Languages : en
Pages :

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Book Description

Author: Nathan Brown
Publisher: Royal Society of Chemistry
ISBN: 1839160543
Category : Computers
Languages : en
Pages : 425

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Book Description
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Author: Csaba Grossi
Publisher: Springer Nature
ISBN: 3031015517
Category : Computers
Languages : en
Pages : 89

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Book Description
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration