Introduction to Simulation Techniques on the Sinclair QL PDF Download
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Author: John Cochrane
Publisher: Heinemann Educational Publishers
ISBN: 9780946408450
Category : Digital computer simulation
Languages : en
Pages : 125
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Book Description
Author: John Cochrane
Publisher: Heinemann Educational Publishers
ISBN: 9780946408450
Category : Digital computer simulation
Languages : en
Pages : 125
Get Book Here
Book Description
Author: Arthur James Wells
Publisher:
ISBN:
Category : English literature
Languages : en
Pages : 1706
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Author:
Publisher:
ISBN:
Category : Great Britain
Languages : en
Pages : 1444
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Author:
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 854
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Book Description
Author:
Publisher:
ISBN:
Category : Best books
Languages : en
Pages : 1044
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Author: British Council
Publisher:
ISBN:
Category : Best books
Languages : en
Pages : 1046
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Book Description
Author:
Publisher:
ISBN:
Category : Great Britain
Languages : en
Pages : 1736
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Book Description
Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433
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Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author:
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Category : Archaeology
Languages : en
Pages : 286
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Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
