Deep learning for coders with fastai and PyTorch aI applications without a PhD Jeremy Howard and Sylvain Gugger
Material type:
TextLanguage: English Publication details: Beijing Boston Farnham Sebastopol Tokyo O'Reilly July 2020 Edition: First editionDescription: xxii, 594 SeitenContent type: Text Media type: ohne Hilfsmittel zu benutzen Carrier type: BandISBN: 978-1-4920-4552-6Subject(s): Neural networks (Computer science) | Machine learning | Python (Computer program language) | Artificial intelligence | Python | Deep learningDDC classification: 006.3/2 LOC classification: QA76.87Other classification: ST 250 | ST 300 Summary: 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
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
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Monographie ausleihbar
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Gemeinsame Bibliothek Lesesaal | M 23.95056 (Browse shelf(Opens below)) | Checked out | 21/11/2023 | 000627634 |
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
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