Name: Advances in Data Science: Women in Data Science and Mathematics (Wisdm) 2023 (Association for Women in Mathematics Series, 37) (en Inglés)
SKU: 64000399
Price: 564.27 PEN
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Author: Garcia-Cardona Cristina,Lee Harlin
ISBN: 9783031878039

portada Advances in Data Science: Women in Data Science and Mathematics (Wisdm) 2023 (Association for Women in Mathematics Series, 37) (en Inglés)

Advances in Data Science: Women in Data Science and Mathematics (Wisdm) 2023 (Association for Women in Mathematics Series, 37) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Garcia-Cardona Cristina,Lee Harlin

Idioma

Inglés

Encuadernación

Tapa Dura

ISBN13

9783031878039

Advances in Data Science: Women in Data Science and Mathematics (Wisdm) 2023 (Association for Women in Mathematics Series, 37) (en Inglés)

Garcia-Cardona Cristina,Lee Harlin (Autor) · Springer · Tapa Dura

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Reseña del libro "Advances in Data Science: Women in Data Science and Mathematics (Wisdm) 2023 (Association for Women in Mathematics Series, 37) (en Inglés)"

This volume features recent advances in data science ranging from algebraic geometry used for existence and uniqueness proofs of low rank approximations for tensor data, to category theory used for natural language processing applications, to approximation and optimization frameworks developed for convergence and robustness guarantees for deep neural networks. It provides ideas, methods, and tools developed in inherently interdisciplinary research problems requiring mathematics, computer science and data domain expertise. It also presents original results tackling real-world problems with immediate applications in industry and government.Contributions are based on the third Women in Data Science and Mathematics (WiSDM) Research collaboration Workshop that took place between August 7 and August 11, 2023 at the Institute for Pure & Applied Mathematics (IPAM) in Los Angeles, California, US. The submissions from the workshop and related groups constitute a valuable source for readers who are interested in mathematically-founded approaches to modeling data for exploration, understanding and prediction.