Publicado por Springer Nature Switzerland, Springer International Publishing, 2025
ISBN 10: 3031897064 ISBN 13: 9783031897061
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 80,24
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability.Clear examples are supported with detailed figures and Python code; Jupyter not Elektronisches Buch and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level.
Publicado por Springer Nature Switzerland, Springer International Publishing Mai 2025, 2025
ISBN 10: 3031897064 ISBN 13: 9783031897061
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 80,24
Convertir monedaCantidad disponible: 2 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability.Clear examples are supported with detailed figures and Python code; Jupyter not Elektronisches Buch and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level. 592 pp. Englisch.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 66,23
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Publicado por Springer Nature Switzerland, Springer International Publishing Mai 2025, 2025
ISBN 10: 3031897064 ISBN 13: 9783031897061
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 80,24
Convertir monedaCantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 592 pp. Englisch.