All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

  • 1
    Call number: 9783958457010 (ebook)
    Description / Table of Contents: Mathematische Grundlagen für Machine und Deep LearningUmfassende Behandlung zeitgemäßer Verfahren: tiefe Feedforward-Netze, Regularisierung, Performance-Optimierung sowie CNNs, Rekurrente und Rekursive Neuronale NetzeZukunftsweisende Deep-Learning-Ansätze sowie von Ian Goodfellow neu entwickelte Konzepte wie Generative Adversarial NetworksDeep Learning ist ein Teilbereich des Machine Learnings und versetzt Computer in die Lage, aus Erfahrungen zu lernen. Dieses Buch behandelt umfassend alle Aspekte, die für den Einsatz und die Anwendung von Deep Learning eine Rolle spielen: In Teil I erläutern die Autoren die mathematischen Grundlagen für Künstliche Intelligenz, Neuronale Netze, Machine Learning und Deep Learning.In Teil II werden die aktuellen in der Praxis genutzten Verfahren und Algorithmen behandelt.In Teil III geben die Autoren Einblick in aktuelle Forschungsansätze und zeigen neue zukunftsweisende Verfahren auf.Dieses Buch richtet sich an Studenten und alle, die sich in der Forschung mit Deep Learning beschäftigen sowie an Softwareentwickler und Informatiker, die Deep Learning für eigene Produkte oder Plattformen einsetzen möchten. Dabei werden Grundkenntnisse in Mathematik, Informatik und Programmierung vorausgesetzt.Teil I: Angewandte Mathematik und Grundlagen für das Machine LearningLineare AlgebraWahrscheinlichkeits- und InformationstheorieBayessche StatistikNumerische BerechnungTeil II: Deep-Learning-VerfahrenTiefe Feedforward-NetzeRegularisierungOptimierung beim Trainieren tiefer ModelleConvolutional Neural NetworksSequenzmodellierung für Rekurrente und Rekursive NetzePraxisorientierte MethodologieAnwendungen: Computer Vision, Spracherkennung, Verarbeitung natürlicher SpracheTeil III: Deep-Learning-ForschungLineare FaktorenmodelleAutoencoderRepresentation LearningProbabilistische graphische ModelleMonte-Carlo-VerfahrenDie PartitionsfunktionApproximative InferenzTiefe generative Modelle wie Restricted Boltzmann Machines, Deep-Belief-Netze, Gerichtete Generative Netze, Variational Autoencoder u.v.m.
    Type of Medium: 12
    Pages: 1 Online-Ressource (xxii, 883 Seiten) , Illustrationen, Diagramme
    Edition: 1. Auflage
    ISBN: 3958457002 , 9783958457003 , 9783958457010 (electronic) , 9783958457027 (electronic)
    Language: German
    Note: Einleitung --- I Angewandte Mathematik und Grundlagen für das Machine Learning --- Lineare Algebra --- Wahrscheinlichkeits- und Informationstheorie --- Numerische Berechnung --- Grundlagen für das Machine Learning --- II Tiefe Netze: Zeitgemäße Verfahren --- Tiefe Feedforward-Netze --- Regularisierung --- Optimierung beim Trainieren von tiefen Modellen --- CNNs --- Sequenzmodellierung: RNNs und rekursive Netze --- Praxisorientierte Methodologie --- Anwendungen --- III Deep-Learning-Forschung --- Lineare Faktorenmodelle --- Autoencoder --- Representation Learning --- Strukturierte probabilistische Modelle für Deep Learning --- Monte-Carlo-Verfahren --- Die Partitionsfunktion --- Approximative Inferenz --- Tiefe generative Modelle --- Literaturverzeichnis --- Abkürzungsverzeichnis --- Index
    Branch Library: GFZ Library
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    ISSN: 1573-4919
    Keywords: ecto-apyrase ; ecto-ATPdiphosphohydrolase ; ecto-ATPase ; apyrase phosphorylation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Chemistry and Pharmacology , Medicine
    Notes: Abstract Ecto-apyrase is a transmembrane glycoprotein that hydrolyzes extracellular nucleoside tri- or diphosphates. Apyrase activity is affected by several physiological and pathological conditions indicating the existence of regulatory mechanisms. Considering that apyrase presents consensus phosphorylation sites, we studied the phosphorylation of this enzyme. We found an overlay of the immunoblotting and phosphorylated bands in three different preparations from rat brain: (a) hippocampal slices, (b) synaptic plasma membrane fragments and (c) cultured astrocytes. In addition, two-dimensional electrophoresis separations with human astrocytoma cells were done to identify unequivocally the coincidence between the immunodetected and phosphorylated protein. These observations indicate that apyrase can be detected as a phosphoprotein, with obvious implications in the regulation of this enzyme.
    Type of Medium: Electronic Resource
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2015-07-08
    Description: Permafrost-related processes drive regional landscape dynamics in the Arctic terrestrial system. A better understanding of past periods indicative of permafrost degradation and aggradation is important for predicting the future response of Arctic landscapes to climate change. Here, we used a multi-proxy approach to analyse a ~ 4 m long sediment core from a drained thermokarst lake basin on the northern Seward Peninsula in western Arctic Alaska (USA). Sedimentological, biogeochemical, geochronological, micropalaeontological (ostracoda, testate amoebae) and tephra analyses were used to determine the long-term environmental Early-Wisconsin to Holocene history preserved in our core for central Beringia. Yedoma accumulation dominated throughout the Early to Late-Wisconsin but was interrupted by wetland formation from 44.5 to 41.5 ka BP. The latter was terminated by the deposition of 1 m of volcanic tephra, most likely originating from the South Killeak Maar eruption at about 42 ka BP. Yedoma deposition continued until 22.5 ka BP and was followed by a depositional hiatus in the sediment core between 22.5 and 0.23 ka BP. We interpret this hiatus as due to intense thermokarst activity in the areas surrounding the site, which served as a sediment source during the Late-Wisconsin to Holocene climate transition. The lake forming the modern basin on the upland initiated around 0.23 ka BP and drained catastrophically in spring 2005. The present study emphasises that Arctic lake systems and periglacial landscapes are highly dynamic and that permafrost formation as well as degradation in central Beringia was controlled by regional to global climate patterns as well as by local disturbances. Copyright © 2015 John Wiley & Sons, Ltd.
    Print ISSN: 1045-6740
    Electronic ISSN: 1099-1530
    Topics: Geography , Geosciences
    Published by Wiley-Blackwell
    Location Call Number Expected Availability
    BibTip Others were also interested in ...