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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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The MIT Press (edition ), 2022
ISBN 10: 0262046822
ISBN 13: 9780262046824
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Hardcover. Condizione: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Codice articolo 0262046822-11-1
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probabilistic machine learning an introduction
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) [Hardcover] Murphy, Kevin P.
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Hardcover. Condizione: New. Brand new. Excellent customer service. Codice articolo 9780262046824
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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hardcover. Condizione: New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Codice articolo 009468670N
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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Condizione: Used - Very Good. 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. Codice articolo Y2-BZI8-O6YW
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Probabilistic Machine Learning : An Introduction
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Probabilistic Machine Learning
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEOCT25-67715
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Probabilistic Machine Learning
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEOCT25-4000
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PROBABILISTIC MACHINE LEARNING: AN INTRODUCTION
Edizione InternazionaleDa: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Condizione: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. Codice articolo ABBB-500054
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Probabilistic Machine Learning
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Codice articolo ABEOCT25-332627
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