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Die Informationsbroschüre „Anforderungen an die Gestaltung multimodaler Mobilitätsanwendungen“ richtet sich an IT-Dienstleister. In dieser Broschüre werden mögliche Potenziale im Bereich des allgemeinen Mobilitätsmanagements aufgezeigt. Automobilhersteller vernetzten sich zunehmend mit Technologie-Unternehmen. Es geht nicht nur um die besondere Entwicklung von spezieller Elektronik- und Softwarelösungen für Navigations- und Entertainmentsysteme oder auch Fahrassistenz-Systemen in modernen PKW, sondern um einen übergreifenden Design- und Interaktionsansatz für miteinander vernetzte Geräte.
With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Analog anzeigende Digitaluhr
(1983)