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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning) - Hardcover
Synopsis
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. <p/>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. <p/>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.
"synopsis" may belong to another edition of this title.
About the Author
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.
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2022
ISBN 10: 0262046822
ISBN 13: 9780262046824
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2022
ISBN 10: 0262046822
ISBN 13: 9780262046824
Used
Hardcover
Seller: BooksRun, Philadelphia, PA, U.S.A.
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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Probabilistic Machine Learning: An Introduction
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Hardback. Condition: 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. Seller Inventory # CIN0262046822G
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Seller: HPB-Red, Dallas, TX, U.S.A.
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_483508507
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2022
ISBN 10: 0262046822
ISBN 13: 9780262046824
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Hardcover
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Published by
The MIT Press (edition ), 2022
ISBN 10: 0262046822
ISBN 13: 9780262046824
Used
Hardcover
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: 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. Seller Inventory # 0262046822-10-1-NAU
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Probabilistic Machine Learning: An Introduction
Seller: World of Books Inc, Montgomery, IL, U.S.A.
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Hardback. Condition: 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. Seller Inventory # CIN0262046822G
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) [Hardcover] Murphy, Kevin P.
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