Deep learning for physics research / Martin Erdmann, RWTH Aachen University, Germany, Jonas Glombitza, RWTH Aachen University, Germany, Gregor Kasieczka, University of Hamburg, Germany, Uwe Klemradt, RWTH Aachen University, Germany.
By: Erdmann, Martin [author.].
Publisher: New Jersey : World Scientific, ©2021Description: 327 p.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811237454.Subject(s): Physics -- Data processing | Physics -- Research | Machine learningGenre/Form: Print books.Summary: "A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | QC52 .E73 2021 (Browse shelf) | Available | AU00000000018628 |
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Includes bibliographical references and index.
"A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded"--