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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Zustand: New. pp. 644.

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Zustand: New. 2015. Softcover reprint of the original 1st ed. 2013. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. reprint edition. 643 pages. 9.25x6.10x1.57 inches. In Stock.

Taschenbuch. Zustand: Neu. A Mathematical Introduction to Compressive Sensing | Simon Foucart (u. a.) | Taschenbuch | Applied and Numerical Harmonic Analysis | xviii | Englisch | 2015 | Humana | EAN 9781493900633 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians.A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

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Zustand: New. 2013. 2013th Edition. Hardcover. This book introduces and explains the essential theory of compressive sensing from a mathematical perspective, offering a substantial collection of exercises with hints to solutions, along with MATLAB codes for algorithms and simulations. Series: Applied and Numerical Harmonic Analysis. Num Pages: 625 pages, 13 black & white illustrations, biography. BIC Classification: PBKF; PDE; TJK; TTBM; UYAM. Category: (P) Professional & Vocational. Dimension: 241 x 162 x 39. Weight in Grams: 1088. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 2013 edition. 605 pages. 9.20x6.30x1.60 inches. In Stock.

Zustand: New. In.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians.A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

Taschenbuch. Zustand: Neu. Explorations in the Mathematics of Data Science | The Inaugural Volume of the Center for Approximation and Mathematical Data Analytics | Simon Foucart (u. a.) | Taschenbuch | Applied and Numerical Harmonic Analysis | xiv | Englisch | 2025 | Birkhäuser | EAN 9783031664991 | Verantwortliche Person für die EU: Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA's inaugural conference - held in May 2023 - and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA's inaugural conference - held in May 2023 - and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory.

Hardcover. Zustand: Brand New. 300 pages. 9.25x6.10x9.21 inches. In Stock.

Zustand: New. 2024. 2024th Edition. hardcover. . . . . . Books ship from the US and Ireland.

Couverture rigide. Zustand: Très bon. Edition originale. Birkhäuser, Applied and numerical harmonic analysis, 2013. In-8, XVIII+625pp. Plein cartonnage de l'éditeur (hardcover). Très bon état.