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 Applied linear analysis for chemical engineers: a multi-scale approach with Mathematica
Balakotaiah V., Ratnakar R., DE GRUYTER, Berlin, Germany, 2022. 590 pp. Type: Book (3110739690)
This 750-plus-page book on applied linear analysis is the culmination of the authors’ more than three decades of experience teaching graduate students in chemical engineering, as well as a continuation of their own mentors’ legacy. It ...
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May 2 2023 |
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Advances in data science
Demir I., Lou Y., Wang X., Welker K., Springer International Publishing, Cham, Switzerland, 2021. 384 pp. Type: Book (978-3-030798-90-1)
Advances in data science, published as volume 26 in Springer’s “Association for Women in Mathematics” series, summarizes the results from two related workshops--the first one held at the Institute for Computational an...
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Feb 21 2023 |
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Foundations of computational imaging: a model-based approach
Bouman C., SIAM, Philadelphia, PA, 2022. 349 pp. Type: Book (1611977126)
This attractively titled book deals with the process of creating images using raw noisy data from unknown sources such as a black hole, for example. This book arose out of the notes used for a graduate-level course on this topic for over two decad...
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Dec 30 2022 |
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Social big data analytics: practices, techniques, and applications
Abu-Salih B., Wongthongtham P., Zhu D., Yan Chan K., Rudra A., Springer International Publishing, Cham, Switzerland, 2021. 228 pp. Type: Book (978-3-030686-50-5)
Thanks to the advances in wireless sensor communication, large-scale storage, and computing technologies, those who can afford to be in one or more of the available platforms--the Internet, WhatsApp, or Twitter, to name a few--are connec...
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Nov 8 2022 |
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Mathematical foundations of big data analytics
Shikhman V., Müller D., Springer International Publishing, New York, NY, 2021. 288 pp. Type: Book (978-3-662625-20-0), Reviews: (1 of 2)
Mathematical foundations of big data analytics is a very welcome and timely addition to the growing area of big data analytics. The authors develop--and strictly follow in each of the nine chapters--a template ...
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Jul 5 2021 |
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Computational methods for deep learning: theoretic, practice and applications
Yan W., Springer International Publishing, Cham, Switzerland, 2021. 211 pp. Type: Book (978-3-030610-80-7), Reviews: (3 of 3)
Computational methods for deep learning is written for the typical second-year graduate student at a US university, working in this area for his/her PhD-level dissertation research on the application of various manifestations of...
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Apr 23 2021 |
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Computational methods for deep learning: theoretic, practice and applications
Yan W., Springer International Publishing, Cham, Switzerland, 2021. 211 pp. Type: Book (978-3-030610-80-7), Reviews: (1 of 3)
Computational methods for deep learning is written for the typical second-year graduate student at a US university, working in this area for his/her PhD-level dissertation research on the application of various manifestations of...
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Apr 23 2021 |
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Computational methods for deep learning: theoretic, practice and applications
Yan W., Springer International Publishing, Cham, Switzerland, 2021. 211 pp. Type: Book (978-3-030610-80-7), Reviews: (2 of 3)
Computational methods for deep learning is written for the typical second-year graduate student at a US university, working in this area for his/her PhD-level dissertation research on the application of various manifestations of...
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Apr 23 2021 |
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Number theory for computing
Yan S., Springer-Verlag New York, Inc., Secaucus, NJ, 2002. 445 pp. Type: Book (9783540430728)
This is an excellent and timely addition to the growing body of literature on computational number theory. It has a very pleasant mix of basic theory, numerous examples that supplement the theory, and computational insights that natur...
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Oct 29 2002 |
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Macro-Star Networks: Efficient Low-Degree Alternatives to Star Graphs
Yeh C., Varvarigos E. IEEE Transactions on Parallel and Distributed Systems 9(10): 987-1003, 1998. Type: Article
This paper introduces an extension of star graphs, called macro-star graphs, based on an interesting combinatorial paradigm. The authors show that the graphs in this class have sublogarithmic diameter, which is asymptotically optimal. ...
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May 1 1999 |
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