Название: An Introduction to Statistical Learning: with Applications in PythonАвтор: Gareth James, Daniela Witten, Trevor Hastie
Издательство: Springer
Год: 2023
Страниц: 617
Язык: английский
Формат: pdf (true)
Размер: 12.6 MB
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
However, in recent years Python has become an increasingly popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book, An Introduction to Statistical Learning, With Applications in Python (ISLP), covers the same materials as ISLR but with labs implemented in Python — a feat accomplished by the addition of a new co-author, Jonathan Taylor. Several of the labs make use of the ISLP Python package, which we have written to facilitate carrying out the statistical learning methods covered in each chapter in Python. These labs will be useful both for Python novices, as well as experienced users.
In this chapter we discuss the basics of neural networks and Deep Learning, and then go into some of the specializations for specific problems, such as convolutional neural networks (CNNs) for image classification, and recurrent neural networks (RNNs) for time series and other sequences. We will also demonstrate these models using the Python torch package, along with a number of helper packages.
The intention behind ISLP (and ISLR) is to concentrate more on the applications of the methods and less on the mathematical details, so it is appropriate for advanced undergraduates or master’s students in statistics or related quantitative fields, or for individuals in other disciplines who wish to use statistical learning tools to analyze their data. It can be used as a textbook for a course spanning two semesters.
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