Up and running with PyTorch

• Titel: Up and running with PyTorch
• Mitwirkende: Dinu, Jonathan [MitwirkendeR]
• Institutionen: Pearson (Firm), [Verlag]
• Ausgabe: [First edition].
• Verlagsort: [Place of publication not identified]
• Verlag: Pearson
• E-Jahr: 2025
• Jahr: [2025]
• Umfang: 1 online resource (1 video file (2 hr., 56 min.))
• Illustrationen: sound, color
• Fussnoten: Online resource; title from title details screen (O’Reilly, viewed March 17, 2025)
• ISBN: 978-0-13-544661-4
• ISBN: 0-13-544661-9
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Apprentissage profond
• Sach-SW: Python (Langage de programmation)
• Form-SW: Instructional films
• Form-SW: Nonfiction films
• Form-SW: Internet videos
• Form-SW: Films de formation
• Form-SW: Films autres que de fiction
• Form-SW: Vidéos sur Internet
• K10plus-PPN: 1921167696
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4696521753
• Lokale URL Inst.: Zum Volltext

Abstract

Up and Running with PyTorch begins with an introduction to PyTorch and deep learning frameworks in general. You learn how the combination of automatic differentiation and transparent computation on GPUs have really enabled the current explosion of deep learning research. The video shows how you can use PyTorch to implement and learn a linear regression model as a stepping stone to building much more complex neural networks. Finally, the video demonstrates how to combine all of the components that PyTorch provides to build a simple feedforward multi-layer perceptron.