01541nam 2200445 450 991037644310332120230808201853.01-4503-4787-8(CKB)3710000001156516(WaSeSS)IndRDA00103630(EXLCZ)99371000000115651620180823d2016 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierRTNS 2016 proceedings of the 24th International Conference on Real-Time Networks and Systems : 19-21 October 2016, Brest, France /general chairs, Alain Plantec, Frank SinghoffNew York :ACM,2016.1 online resource (353 pages)ACM International Conference Proceedings SeriesIncludes index.ACM international conference proceedings series.Real-Time Networks and Systems 2016Proceedings of the 24th International Conference on Real-Time Networks and SystemsReal-time data processingCongressesMultiprocessorsCongressesSchedulingComputer programsCongressesReal-time data processingMultiprocessorsSchedulingComputer programs004.33Plantec AlainSinghoff FrankWaSeSSWaSeSSBOOK9910376443103321RTNS 20162244367UNINA02322nam 2200493 450 991083018810332120210208121211.01-119-56664-91-5231-2825-91-119-56663-01-119-56661-4(MiAaPQ)EBC5808408(OCoLC)1107493596(EXLCZ)99410000000869367720190722d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierHigh voltage direct current transmission converters, systems and DC grids /Dragan Jovcic, University of Aberdeen, Aberdeen, ScotlandSecond edition.Hoboken, New Jersey :Wiley,[2019]©20191 online resource (688 pages)1-119-56654-1 Includes bibliographical references and index."The book describes HVDC (High Voltage Direct Current) Transmission technologies including LCC (Line Commutated Converter) HVDC, VSC (Voltage Source Converter) HVDC and the latest VSC HVDC based on MMC (modular multilevel converters), as well as the principles of building DC transmission grids. When the first edition was written, the MMC converters were just being introduced, but nowadays they have become standard HVDC technology. In the second edition many updates will be made and several new chapters/sections will be introduced, particularly, the latest MMC converters will be covered in more depth. Also, DC switchgear and DC grid protection will be expanded following the latest developments on the market and in research projects"--Provided by publisher.Electric power distributionDirect currentElectric power distributionHigh tensionElectric current convertersElectric power distributionDirect current.Electric power distributionHigh tension.Electric current converters.621.31912Jovcic Dragan1608353MiAaPQMiAaPQMiAaPQCaOWtUBOOK9910830188103321High voltage direct current transmission3935036UNINA04266nam 22006015 450 991036990220332120200702232745.09781484253731148425373610.1007/978-1-4842-5373-1(CKB)4100000009844914(DE-He213)978-1-4842-5373-1(MiAaPQ)EBC5979665(CaSebORM)9781484253731(OCoLC)1139336072(OCoLC)on1139336072(EXLCZ)99410000000984491420191116d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierHands-on Scikit-Learn for Machine Learning Applications Data Science Fundamentals with Python /by David Paper1st ed. 2020.Berkeley, CA :Apress :Imprint: Apress,2020.1 online resource (XIII, 242 p. 33 illus.) Includes index.9781484253724 1484253728 1. Introduction to Scikit-Learn -- 2. Classification from Simple Training Sets -- 3. Classification from Complex Training Sets -- 4. Predictive Modeling through Regression -- 5. Scikit-Learn Classifier Tuning from Simple Training Sets -- 6. Scikit-Learn Classifier Tuning from Complex Training Sets -- 7. Scikit-Learn RegressionTuning -- 8. Putting it All Together.Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats.Machine learningPython (Computer program language)Big dataMachine Learninghttps://scigraph.springernature.com/ontologies/product-market-codes/I21010Pythonhttps://scigraph.springernature.com/ontologies/product-market-codes/I29080Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Machine learning.Python (Computer program language)Big data.Machine Learning.Python.Big Data.006.31Paper Davidauthttp://id.loc.gov/vocabulary/relators/aut995402MiAaPQMiAaPQMiAaPQBOOK9910369902203321Hands-on Scikit-Learn for Machine Learning Applications2280497UNINA