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Periodical
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Periodical
ISSN: 25043315 Year: 1956 Publisher: Schaffhausen : Naturforschende Gesellschaft Schaffhausen,
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ISSN: 25714864 Year: 1953 Publisher: Zürich : ETH Bibliothek,
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Prisma bezeichnet sich als populärwissenschaftliche Zeitschrift, welche einen Überblick über das ganze weite Gebiet von Physik, Chemie, Astronomie, Biologie, Medizin und Technik geben will. Das Ziel der Zeitschrift ist es, die behandelten Themen allgemeinverständlich, aber immer wissenschaftlich korrekt, darzustellen. Prisma informiert über den Stand er wissenschaftlichen Forschung und die Arbeit in den Laboratorien und Industriebetrieben in der Schweiz und im Ausland.
Book
ISBN: 3958647391 9700000036343 Year: 2011 Publisher: [Place of publication not identified] : Perlego,
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ISBN: 0674066405 9780674066403 0674011201 9780674011205 Year: 2012 Publisher: Cambridge, MA
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Each of these essays struggles in one way or another with the necessity of facing up to the discovery that the laws of nature are impersonal, with no hint of a special status for human beings. Defending the spirit of science against its cultural adversaries, these essays express a viewpoint that is reductionist, realist, and devoutly secular. Together, they afford the general reader the unique pleasure of experiencing the superb sense, understanding, and knowledge of one of the most interesting and forceful scientific minds of our era.ease fill in marketing copy
Book
ISBN: 147185633X 9781471856334 1471856313 Year: 2015 Publisher: London, England : Galore Park Publishing Limited,
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ISBN: 9781471880384 1471880389 1471880370 9781471880377 Year: 2016 Publisher: London, [England] : Dynamic Learning : Hodder Education,
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ISSN: 22501754 Year: 1978 Publisher: [Allahabad] National Academy of Sciences.
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ISSN: 22501746 Year: 1942 Publisher: Allahabad [India] : Council of the National Academy of Sciences, India,
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ISBN: 1484271505 1484271491 Year: 2021 Publisher: Berkeley, CA : Apress L. P.,
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Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.
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