Understanding Machine Learning From Theory to Algorithms


Understanding Machine Learning: From Theory to Algorithms
By 作者: Shai Shalev-Shwartz - Shai Ben-David
ISBN-10 书号: 1107057132
ISBN-13 书号: 9781107057135
Edition 版本: 1
Release Finelybook 出版日期: 2014-05-19
Pages 页数: (410 )

The Book Description robot was collected from Amazon and arranged by Finelybook

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.


下载地址

Understanding Machine Learning From Theory to Algorithms 9781107057135.pdf

下载地址 DOWNLOAD隐藏内容需1积分,请先!没有帐号? 注 册 一个!
觉得文章有用就打赏一下文章作者
未经允许不得转载:finelybook » Understanding Machine Learning From Theory to Algorithms
分享到: 更多 (0)

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下文章作者

支付宝扫一扫打赏

微信扫一扫打赏