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  • Safari, an O’Reilly Media Company.  (3)
  • [Erscheinungsort nicht ermittelbar] : Apress  (2)
  • [Erscheinungsort nicht ermittelbar] : dpunkt  (1)
  • [Erscheinungsort nicht ermittelbar] : RIBA Publishing
  • Computer Science  (3)
  • 1
    Online Resource
    Online Resource
    Deep Learning with Python : Learn Best Practices of Deep Learning Models with PyTorch (2021)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484253649
    Language: English
    Pages: 1 online resource (316 pages)
    Edition: 2nd edition
    Parallel Title: Erscheint auch als Ketkar, Nikhil Deep learning with Python
    RVK:
    RB 10104
    RVK:
    ST 302
    Keywords: Electronic books ; local ; Electronic books ; Deep learning ; Python ; PyTorch
    Abstract: Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
    Note: Online resource; Title from title page (viewed April 9, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Laws of UX : 10 praktische Grundprinzipien für intuitives, menschenzentriertes UX-Design (2020)
    [Erscheinungsort nicht ermittelbar] : dpunkt | Boston, MA : Safari
    Details
    Language: English , German
    Pages: 1 Online-Ressource (xiii, 125 Seiten)
    Edition: 1st edition
    Parallel Title: Erscheint auch als Yablonski, Jon Laws of UX
    RVK:
    ST 278
    Keywords: Electronic books ; local ; Mensch-Maschine-Kommunikation ; Webdesign ; Gebrauchsgrafik ; Benutzerfreundlichkeit ; Verhaltenspsychologie
    Abstract: Jon Yablonski erklärt in diesem Buch, wie UX-Designerinnen Grundprinzipien aus der Psychologie nutzen können, um eine bessere User Experience zu generieren. Anstatt Benutzerinnen zu zwingen, sich an das Design eines Produkts (z. B. App) anzupassen, hilft dieser praktische Leitfaden dabei, das Design danach auszurichten, wie Benutzer*innen sich verhalten und mit digitalen Schnittstellen interagieren, um ihre Nutzung einfacher und angenehmer zu gestalten. Dabei greift der Autor auf bekannte Regeln und Prinzipien aus der psychologischen Forschung zurück und überträgt sie in die UX-Design-Welt. So sprechen wir beispielsweise einer App mit schönem Design mehr Kompetenz zu und verzeihen ihr eher Fehler, oder erwarten von einem Onlineshop, dass der Kaufprozess so funktionieren, wie wir es von anderen Shops gewohnt sind. Außerdem können wir eine große Menge an Informationen besser speichern und verarbeiten, wenn sie in Chunks gegliedert sind, weshalb etwa Texte, die mithilfe von Überschriften und Absätzen gegliedert sind, eine höhere UX generieren als ein langer Fließtext, der die User überfordert. Nachdem der Autor die verschiedenen Prinzipien erklärt und an anschaulichen, einfach nachzuvollziehenden Beispielen demonstriert hat, zeigt er, wie man diese Prinzipien praktisch für die eigene Arbeit und im Team nutzen kann. Zusätzlich geht er auch auf die ethischen Komponenten ein (Beispiele: Endlos-Scrollen, Like-Button).
    Note: Online resource; Title from title page (viewed September 30, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Ontologies with Python : Programming OWL 2.0 Ontologies with Python and Owlready2 (2020)
    [Erscheinungsort nicht ermittelbar] : Apress | Boston, MA : Safari
    Details
    ISBN: 9781484265529
    Language: English
    Pages: 1 online resource (353 pages)
    Edition: 1st edition
    Parallel Title: Erscheint auch als Jean-Baptiste, Lamy Ontologies with Python
    RVK:
    ST 250
    Keywords: Electronic books ; local ; Electronic books
    Abstract: Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python. What You Will Learn Use Owlready2 to access and modify OWL ontologies in Python Publish ontologies on dynamic websites Perform automatic reasoning in Python Use well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System) Integrate Python methods in OWL ontologies Who Is This Book For Beginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.
    Note: Online resource; Title from title page (viewed December 17, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
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