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Автор: Changho Suh
Издательство: Now Publishers Inc
Год: 2022
Страниц: 379
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on Machine Learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in Machine Learning and Deep Learning. We employ Python as a major programming platform. To solve traditional convex optimization problems such as linear program, least squares, and semi-definite program, we utilize an easy-to-use and high-level language, CVXPY, running in Python. To implement Machine Learning and Deep Learning algorithms, we employ TensorFlow, one of the most popular Deep Learning frameworks.