Deep Learning for Physical Scientists: Accelerating Research with Machine Learning
Language: English
Published by Wiley, 2021
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
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Add to basketItem description from seller
In English.
Seller Inventory # ria9781119408338_new
- Title
- Deep Learning for Physical Scientists: Accelerating Research with Machine Learning
- Author
- Pyzer-Knapp, Edward O.; Benatan, Matthew
- Publisher
- Wiley
- Publication year
- 2021
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1119408334
- ISBN 13
- 9781119408338
- Item weight
- 564 grams
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field
Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome.
Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader.
From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy:
- A thorough introduction to the basic classification and regression with perceptrons
- An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training
- An examination of multi-layer perceptrons for learning from descriptors and de-noising data
- Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images
- A treatment of Bayesian optimization for tuning deep learning architectures
Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access.
"Synopsis" may belong to another edition of this title.
About the Author
Dr Edward O. Pyzer-Knapp is the worldwide lead for AI Enriched Modelling and Simulation at IBM Research. Previously, he obtained his PhD from the University of Cambridge using state of the art computational techniques to accelerate materials design then moving to Harvard where he was in charge of the day-to-day running of the Harvard Clean Energy Project - a collaboration with IBM which combined massive distributed computing, quantum-mechanical simulations, and machine-learning to accelerate discovery of the next generation of organic photovoltaic materials. He is also the Visiting Professor of Industrially Applied AI at the University of Liverpool, and the Editor in Chief for Applied AI Letters, a journal with a focus on real-world application and validation of AI.
Dr Matt Benatan received his PhD in Audio-Visual Speech Processing from the University of Leeds, after which he went on to pursue a career in AI research within industry. His work to date has involved the research and development of AI techniques for a broad variety of domains, from applications in audio processing through to materials discovery. His research interests include Computer Vision, Signal Processing, Bayesian Optimization, and Scalable Bayesian Inference.
"About the title" may belong to another edition of this title.
Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
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