L'intelligence artificielle pour les nuls en 50 notions clés PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download L'intelligence artificielle pour les nuls en 50 notions clés PDF full book. Access full book title L'intelligence artificielle pour les nuls en 50 notions clés by Thomas Solignac. Download full books in PDF and EPUB format.
Author: Thomas Solignac
Publisher:
ISBN: 9782412094969
Category :
Languages : fr
Pages : 0
Get Book Here
Book Description
Author: Thomas Solignac
Publisher:
ISBN: 9782412094969
Category :
Languages : fr
Pages : 0
Get Book Here
Book Description
Author: Marcelo G. Kohen
Publisher: Cambridge University Press
ISBN: 9780521849289
Category : Law
Languages : en
Pages : 560
Get Book Here
Book Description
This book is a comprehensive study of secession from an international law perspective.
Author: J. Zajda
Publisher: Springer Science & Business Media
ISBN: 1402047223
Category : Education
Languages : en
Pages : 224
Get Book Here
Book Description
This book explores the problematic relationship between education, social justice and the State, against the background of comparative education research. The book critiques the status quo of stratified school systems, and the unequal distribution of cultural capital and value added schooling. The authors address one of today’s most pressing questions: Are social, economic and cultural divisions between the nations, between school sectors, between schools and between students growing or declining?
Author: Montano, Bonnie
Publisher: IGI Global
ISBN: 1591402301
Category : Computers
Languages : en
Pages : 300
Get Book Here
Book Description
Innovations of Knowledge Management highlights the broad range of topics that fall under the term knowledge management, thus emphasizing the large role knowledge management plays in organizations. As a compilation of some of the most recent work in the field, the included chapters truly present innovations in how organizations can and should manage their knowledge.
Author: Neural Information Processing Systems Foundation
Publisher: MIT Press
ISBN: 0262026171
Category : Algorithms
Languages : en
Pages : 361
Get Book Here
Book Description
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
Author: Michael J. Kearns
Publisher: MIT Press
ISBN: 9780262111935
Category : Computers
Languages : en
Pages : 230
Get Book Here
Book Description
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
Author: Vladimir Vapnik
Publisher: Springer Science & Business Media
ISBN: 1475732643
Category : Mathematics
Languages : en
Pages : 324
Get Book Here
Book Description
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Author: James Crawford
Publisher: Oxford University Press
ISBN: 0198260024
Category : Law
Languages : en
Pages : 943
Get Book Here
Book Description
Statehood in the early 21st century remains as much a central problem now as it was in 1979 when the first edition of The Creation of States in International Law was published. As Rhodesia, Namibia, the South African Homelands and Taiwan then were subjects of acute concern, today governments, international organizations, and other institutions are seized of such matters as the membership of Cyprus in the European Union, application of the Geneva Conventions to Afghanistan, a final settlement for Kosovo, and, still, relations between China and Taiwan. All of these, and many other disputed situations, are inseparable from the nature of statehood and its application in practice. The remarkable increase in the number of States in the 20th century did not abate in the twenty five years following publication of James Crawford's landmark study, which was awarded the American Society of International Law Prize for Creative Scholarship in 1981. The independence of many small territories comprising the 'residue' of the European colonial empires alone accounts for a major increase in States since 1979; while the disintegration of Yugoslavia and the USSR in the early 1990s further augmented the ranks. With these developments, the practice of States and international organizations has developed by substantial measure in respect of self-determination, secession, succession, recognition, de-colonization, and several other fields. Addressing such questions as the unification of Germany, the status of Israel and Palestine, and the continuing pressure from non-State groups to attain statehood, even, in cases like Chechnya or Tibet, against the presumptive rights of existing States, James Crawford discusses the relation between statehood and recognition; the criteria for statehood, especially in view of evolving standards of democracy and human rights; and the application of such criteria in international organizations and between states. Also discussed are the mechanisms by which states have been created, including devolution and secession, international disposition by major powers or international organizations and the institutions established for Mandated, Trust, and Non-Self-Governing Territories. Combining a general argument as to the normative significance of statehood with analysis of numerous specific cases, this fully revised and expanded second edition gives a comprehensive account of the developments which have led to the birth of so many new states.
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
Get Book Here
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.
Author: Léon Chertok
Publisher: Pergamon
ISBN:
Category : Psychology
Languages : en
Pages : 200
Get Book Here
Book Description
