Analysis of Criteria for the Selection of Machine Learning Frameworks
- 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.
| Verfasserangaben: | Kai Dinghofer, Frank HartungORCiD |
|---|---|
| DOI: | https://doi.org/10.1109/ICNC47757.2020.9049650 |
| Titel des übergeordneten Werkes (Englisch): | 2020 International Conference on Computing, Networking and Communications (ICNC) |
| Verlag: | IEEE |
| Verlagsort: | New York, NY |
| Dokumentart: | Konferenzveröffentlichung |
| Sprache: | Englisch |
| Erscheinungsjahr: | 2020 |
| Datum der Publikation (Server): | 03.04.2020 |
| Erste Seite: | 373 |
| Letzte Seite: | 377 |
| Bemerkung: | 2020 International Conference on Computing, Networking and Communications (ICNC), 17-20 February 2020, Big Island, HI, USA |
| Link: | https://doi.org/10.1109/ICNC47757.2020.9049650 |
| Zugriffsart: | campus |
| Fachbereiche und Einrichtungen: | FH Aachen / ECSM European Center for Sustainable Mobility |
| FH Aachen / Fachbereich Elektrotechnik und Informationstechnik | |
| collections: | Verlag / IEEE |
