Deep Learning for Coders with fastai and PyTorch

• Verfasst von: Howard, Jeremy [VerfasserIn]
• Verfasst von: Gugger, Sylvain [VerfasserIn]
• Titel: Deep Learning for Coders with fastai and PyTorch
• Institutionen: Safari, an O’Reilly Media Company.
• Verf.angabe: Howard, Jeremy
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: O'Reilly Media, Inc.
• Jahr: 2020
• Umfang: 1 online resource (550 pages)
• Fussnoten: Online resource; Title from title page (viewed June 25, 2020)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic books ; local
• Sach-SW: Electronic books
• K10plus-PPN: 1693203006
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3612465929
• Lokale URL Inst.: Zum Volltext

Abstract

Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you’ll learn how to train a model to accomplish a wide range of tasks—including computer vision, natural language processing, tabular data, and generative networks. At the same time, you’ll dig progressively into deep learning theory so that by the end of the book you’ll have a complete understanding of the math behind the library’s functions.