Probabilistic Machine Learning: An Introduction (Hardcover)
Murphy, Kevin P.
- 出版商: Summit Valley Press
- 出版日期: 2022-03-01
- 售價: $2,650
- 貴賓價: 9.8 折 $2,597
- 語言: 英文
- 頁數: 864
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0262046822
- ISBN-13: 9780262046824
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相關分類:
Machine Learning
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相關主題
商品描述
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
商品描述(中文翻譯)
一本詳盡且最新的機器學習介紹書,以概率建模和貝葉斯決策理論為統一視角。
本書通過概率建模和貝葉斯決策理論的統一視角,提供了一個詳盡且最新的機器學習介紹,包括深度學習。書中涵蓋了數學背景(包括線性代數和優化)、基礎監督學習(包括線性回歸、邏輯回歸和深度神經網絡),以及更高級的主題(包括遷移學習和無監督學習)。章末練習題讓學生能夠應用所學知識,附錄則涵蓋了符號表示法。
《概率機器學習》是作者2012年的書籍《機器學習:一個概率的視角》的延伸。這不僅僅是一本簡單的更新,而是一本全新的書籍,反映了自2012年以來該領域的重大發展,尤其是深度學習。此外,新書還附帶了在線Python代碼,使用scikit-learn、JAX、PyTorch和Tensorflow等庫,可以重現幾乎所有圖表;這些代碼可以在基於雲的筆記本中在網頁瀏覽器中運行,並提供了對書中理論主題的實際補充。這本入門教材將會有一本續集,涵蓋更高級的主題,採用相同的概率方法。
作者簡介
Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.
作者簡介(中文翻譯)
Kevin P. Murphy是加州山景城Google的研究科學家,他在AI、機器學習、計算機視覺和自然語言理解方面進行研究工作。