Machine learning for the physical sciences : fundamentals and prototyping with Julia / Carlo Requião Da Cunha.
"Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and...
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| Online Access: |
Full Text (via Taylor & Francis) |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
2024.
|
| Edition: | First edition. |
| Subjects: |
MARC
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| 100 | 1 | |a Da Cunha, Carlo Requião, |e author. | |
| 245 | 1 | 0 | |a Machine learning for the physical sciences : |b fundamentals and prototyping with Julia / |c Carlo Requião Da Cunha. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c 2024. | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applications and advancements in their fields. This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demonstrates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers"-- |c Provided by publisher. | ||
| 545 | 0 | |a Carlo R. da Cunha is currently an assistant professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. He holds a Ph.D. degree in electrical engineering from Arizona State University. Throughout his career, Dr. da Cunha has held various academic positions and research affiliations in institutions such as McGill University, Chiba University, and the Technical University of Vienna. His research focuses on computational science, where he applies machine learning techniques to the design of innovative electronic devices and systems. | |
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Physical sciences |x Data processing |v Textbooks. | |
| 650 | 0 | |a Machine learning |v Textbooks. | |
| 650 | 0 | |a Julia (Computer program language) |v Textbooks. | |
| 650 | 7 | |a Julia (Computer program language) |2 fast |0 (OCoLC)fst01938397 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Physical sciences |x Data processing. |2 fast |0 (OCoLC)fst01062726 | |
| 655 | 7 | |a Textbooks. |2 fast |0 (OCoLC)fst01423863 | |
| 776 | 0 | 8 | |i Print version: |a Da Cunha, Carlo Requião. |t Machine learning for the physical sciences. |b First edition. |d Boca Raton, FL : CRC Press, 2024 |z 9781032392295 |w (DLC) 2023031643 |w (OCoLC)1404059201 |
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