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Books on the topic 'Algorithme de tri'

Author: Grafiati

Published: 9 November 2024

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1

Beauquier, Danièle. Eléments d'algorithmique. Paris: Masson, 1992.

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2

Siomina, Iana. Radio network planning and resource optimization: Mathematical models and algorithms for UMTS, WLANs, and ad hoc networks. Norrko ping: Department of Science and Technology, Linko ping University, 2007.

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3

1957-, Leporini Dino, ed. Capra e calcoli: L'eterna lotta tra gli algoritmi e il caos. Bari: Laterza, 2014.

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4

International Conference on Tools for Artificial Intelligence (5th 1993 Boston, Mass.). Proceedings: Fifth International Conference on Tools with Artificial Intelligence - TAI '93, November 8-11, 1993, Boston, Massachusetts. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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5

International Conference on Tools for Artificial Intelligence (4th 1992 Arlington, Va.). Proceedings, Fourth International Conference on Tools with Artificial Intelligence: TAI '92, November 10-13, 1992, Arlington, Virginia. Los Alamitos, Calif: IEEE Computer Society Press, 1992.

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6

International, Conference on Tools for Artificial Intelligence (5th 1993 Boston Mass ). Proceedings: Fifth International Conference on Tools with Artificial Intelligence, TAI '93, November 8-11, 1993, Boston, Massachusetts. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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7

Algorithmes et structures de données en langage C: C ANSI et C++. Dunod, 1996.

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8

Tri algoritma estestvennoĭ klasteriza͡tsii obʺektov. Moskva: V͡TS AN SSSR, 1986.

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9

Data Science: Algorithm Analysis, Approximation, Automata, Bayesian Inference, Statistics, Science,TR6 Binary Tree, Bioinformatics, Algorithms, Complexity Theory, Computability and Research. Independently Published, 2022.

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10

Michel, Bierlaire. Optimization: Principles and Algorithms. EPFL Press, 2015. http://dx.doi.org/10.55430/6116v1mb.

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Every engineer and decision scientist must have a good mastery of optimization, an essential element in their toolkit. Thus, this articulate introductory textbook will certainly be welcomed by students and practicing professionals alike. Drawing from his vast teaching experience, the author skillfully leads the reader through a rich choice of topics in a coherent, fluid and tasteful blend of models and methods anchored on the underlying mathematical notions (only prerequisites: first year calculus and linear algebra). Topics range from the classics to some of the most recent developments in smooth unconstrained and constrained optimization, like descent methods, conjugate gradients, Newton and quasi-Newton methods, linear programming and the simplex method, trust region and interior point methods. Furthermore elements of discrete and combinatorial optimization like network optimization, integer programming and heuristic local search methods are also presented. This book presents optimization as a modeling tool that beyond supporting problem formulation plus design and implementation of efficient algorithms, also is a language suited for interdisciplinary human interaction. Readers further become aware that while the roots of mathematical optimization go back to the work of giants like Newton, Lagrange, Cauchy, Euler or Gauss, it did not become a discipline on its own until World War Two. Also that its present momentum really resulted from its symbiosis with modern computers, which made it possible to routinely solve problems with millions of variables and constraints. With his witty, entertaining, yet precise style, Michel Bierlaire captivates his readers and awakens their desire to try out the presented material in a creative mode. One of the outstanding assets of this book is the unified, clear and concise rendering of the various algorithms, which makes them easily readable and translatable into any high level programming language. ''This is an addictive book that I am very pleased to recommend.'' Prof. Thomas M. Liebling

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11

Goodman, Wayne K., and Mark S. George. Neuromodulation and Psychiatric Disorders. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0010.

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An increasing number of approaches permit psychiatrists to directly stimulate the brain. Such therapies are sometimes referred to as neuromodulation, as psychiatrists can either excite or inhibit neuronal firing in the brain. This chapter reviews two such technologies—transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS). Both techniques have FDA approval and are moving into mainstream therapeutic use. Daily prefrontal TMS for 4–6 weeks is FDA approved for treating depression, with minimal side effects. It is now accepted in most treatment algorithms as an approach for patients who have not responded to medications or talking therapy. DBS has virtually replaced ablative neurosurgery for use in medication-refractory movement disorders such as Parkinson’s Disease (PD), where it has the advantages of being reversible (explantable) and adjustable. DBS is now being studied in severe psychiatric conditions, such as intractable obsessive-compulsive disorder (OCD) and treatment resistant depression (TRD).

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12

Johnson, Chris, Sarah R. Anderson, Jon Dallimore, Chris Imray, Shane Winser, James Moore, and David Warrell. Oxford Handbook of Expedition and Wilderness Medicine. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199688418.001.0001.

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Revised for its second edition to include the latest national and international guidelines, the Oxford Handbook of Expedition and Wilderness Medicine enables efficient preparation and planning before the journey, advises on camp logistics, risk management, and medical problems during the trip, as well as highlighting rare but important risks to those visiting remote areas. Focusing on preventative measures, it also contains chapters dealing with crisis management, emergency care, and evacuation from challenging environments. Now containing more guidance about the obligations of a clinician joining an expedition, and the ethical approach to such work, it also provides an increased emphasis on medicine in various extreme environments. With revised and additional illustrations, more colour plates, and an increased use of important algorithms, it has been updated with the support of the Royal Geographical Society, and incorporates the combined knowledge and experience of a team of experienced clinicians and expeditioners.

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13

Helmchen, Hanfried. Fuzzy boundaries and tough decisions in psychiatry. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198722373.003.0007.

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In the field of mental disorders the boundaries between mental normality and disease are often blurred. Psychiatrists, being aware of the instrumental nature of psychiatric diagnoses, try to deal with these blurred boundaries by stipulating strict definitions of clinical categories, by operationalizing diagnoses, and by using evidence-based methods of production and algorithmic application of medical knowledge. However, the clinical uncertainties can only partially be reduced by these means, because the physician always has to consider the individuality of the patient as well as to find a helpful solution to the dilemma of not treating a treatable but only probable or even only possible disease condition versus treating a variation of normal behavior against the risk of obtaining unwanted side effects of drug therapy or stigmatization. As is shown in the chapter, subthreshold mental disorders provide illuminating illustrations of these limits of medical standardization.

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14

80/20: Professional Data Design, Structures, Algorithms, Analysis, Python, SQL, Computer Science, AI,TR5 Technology, R, SAS, Applications, Monetization, Ethics, Warehouse, Capture and Certification. Independently Published, 2022.

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15

Decision Making Handbook: Machine Learning for Absolute Beginners, Finance, Business Math, Statistics, Python, Algorithms,TR6 Applications, Theory, Design Patterns, Engineering, Excel, Math and Mathematics. Independently Published, 2022.

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16

Decision Making Handbook: Machine Learning for Absolute Beginners, Finance, Business Math, Statistics, TR5,Python, Algorithms, Applications, Theory, Design Patterns, Engineering, Excel, Math and Mathematics. Independently Published, 2022.

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17

Decision Making Handbook: Programming Language, Networking Systems, Top-Down Approach, Organization,TR5 Cyber Security, Data, Quick Keys, Repair, Troubleshooting, Vision Metrics, Algorithms and Practice. Independently Published, 2022.

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18

Decision Making Handbook: Machine Learning for Absolute Beginners, Finance, Business Math, TR5, Statistics, Python, Algorithms, Applications, Theory, Design Patterns, Engineering, Excel, Math and Mathematics. Independently Published, 2022.

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19

80/20 Rule: Machine Learning for Absolute Beginners, Finance, Business Principle, Math, Statistics,TR5 Python, Algorithms, Applications, Theory, Design Patterns, Engineering, Excel, Math and Mathematics. Independently Published, 2022.

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20

Leadership, Management and Data Strategy: Programming Language, Networking Systems, Top-Down Approach, Organization,TR5 Cyber Security, Quick Keys, Repair, Troubleshooting, Vision Metrics, Algorithms and Practice. Independently Published, 2022.

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21

Management Science and Engineering: Software, Devops, Computational Geometry, Computer Graphics, Algorithms, TR5, Mathematics, DirectX, Edge Detection, GPU, OpenCV, OpenGL, OpenGL ES, Rendering, Unity3D and WebGL. Independently Published, 2022.

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22

Paulauskaitė-Tarasevičienė, Agnė, and Kristina Šutienė. Intelektikos pagrindai. KTU leidykla „Technologija“, 2022. http://dx.doi.org/10.5755/e01.9786090218068.

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Dirbtinis intelektas (DI) yra daugiausia diskusijų keliantis ir tyrinėjimų reikalaujantis objektas technologijų pasaulyje. DI grįstos skaitmeninės technologijos tampa svarbia kiekvieno iš mūsų kasdienybės dalimi. Daugelis žino apie dirbtinio intelekto programas, palengvinančias mūsų gyvenimą, tačiau mažai kas supranta, kas tai yra iš tiesų. Įdomu tai, kad DI funkcionuoja kelių skirtingų akademinių disciplinų, daugiausia informatikos, matematikos ir inžinerijos, sandūroje. Tačiau sričių, kuriose DI metodai gali būti taikomi, yra labai daug: nuo finansų iki kompiuterinių žaidimų ir t. t. Lietuviško vadovėlio apie DI autorėms teko nelengva užduotis: skaitytoją supažindinti su DI elementais ir suteikti pagrindinių intelektikos metodų žinių taip, kad būtų ir naudinga, ir patrauklu skaityti tiek studentams, tiek įvairių sričių atstovams. Reikia pripažinti, kad nėra vieno universalaus ir visais atvejais gerai veikiančio DI algoritmo. Šiandien jų įvairovė ypač didelė, todėl pirmasis ir vienas iš svarbiausių siekių – pažinti juos, suprasti jų taikymo galimybes ir veikimo principus, kad vėliau būtų gebama atsirinkti tinkamą metodą ar jų grupę sprendžiant konkretų uždavinį. Todėl šiame vadovėlyje pateikiami dažniausiai taikomi DI metodai, kurių paskirtis – klasifikuoti, prognozuoti ar surasti geriausią sprendimą iš visų galimų. Daugelis DI metodų mokosi iš duomenų, todėl skaitytojas supažindinamas, į ką svarbiausia atkreipti dėmesį ruošiant duomenis modeliui mokytis. Autorės vadovėlio medžiagą rekomenduoja naudoti kaip įvadinę priklausomai nuo studijų pakopos ar studijų programos. Šis vadovėlis parengtas taip, kad sau naudingų žinių rastų įvairios srities – inžinerinės, gamtos, verslo ar humanitarinės – studijų studentai ir entuziastai, siekiantys įgyti dirbtinio intelekto kompetencijų.

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23

Leadership, Management and Data Science: Machine Learning for Absolute Beginners, Finance, Business Math, Statistics, Python,TR5 Algorithms, Applications, Theory, Design Patterns, Engineering, Excel, Math and Mathematics. Independently Published, 2022.

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24

Leadership, Management and Data Science: Professional Data Design, Structures, Algorithms, Analysis, Python, SQL, Computer Science, AI,TR5 Technology, R, SAS, Applications, Monetization, Ethics, Warehouse, Capture and Certification. Independently Published, 2022.

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25

Savransky, Semyon D. Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving. Taylor & Francis Group, 2000.

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26

Savransky, Semyon D. Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving. Taylor & Francis Group, 2000.

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27

Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving. CRC, 2000.

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