Название: Algorithmic Mathematics in Machine Learning
Автор: Bastian Bohn, Jochen Garcke, Michael Griebel
Издательство: SIAM (Society for Industrial and Applied Mathematics)
Серия: Data Science Book Series
Год: 2024
Страниц: 235
Язык: английский
Формат: pdf (true)
Размер: 25.5 MB
This unique book explores several well-known Machine Learning and data analysis algorithms from a mathematical and programming perspective. The authors present Machine Learning methods, review the underlying mathematics, and provide programming exercises to deepen the reader's understanding; accompany application areas with exercises that explore the unique characteristics of real-world data sets (e.g., image data for pedestrian detection, biological cell data); and provide new terminology and background information on mathematical concepts, as well as exercises, in “info-boxes” throughout the text. Most chapters of this book will be accompanied by a variety of programming exercises, which are intended to be solved in Jupyter notebooks. There, we also provide templates for some of the programming exercises. We assume that the reader is familiar with basic Python tools and libraries. However, we also provide a brief tutorial on the most important Python and NumPy concepts. Moreover, we will learn how to use the Machine Learning libraries Scikit-Learn and Keras. The latter serves as an interface to Tensor Flow.