物品 #:
128878998
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
87% of respondents would recommend this to a friend
物品 #:
128878998
HKD 1330
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產品詳情
- 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.
| Publisher | The MIT Press |
| Publication date | 1 Mar. 2022 |
| Language | English |
| Print length | 944 pages |
| ISBN-10 | 0262046822 |
| ISBN-13 | 978-0262046824 |
| Dimensions | 21.3 x 3.3 x 23.5 cm |
產品敘述
Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
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English edition Kevin P. Murphy Format: Hardcover Editorial Review
Probabilistic Machine Learning: An Introduction (Adaptive Computation And Machine Learning Series) offers a comprehensive overview of the foundational principles and methods of probabilistic machine learning, making it an essential read for both students and professionals in the field. Published by The MIT Press, this extensive 944-page volume released on March 1, 2022, covers an expansive range of topics. Its in-depth content is suitable for those looking to enhance their knowledge of machine learning theories and techniques. The book has been praised for its clarity and thoroughness, providing readers with robust theoretical insights while maintaining accessibility.
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優點
- Comprehensive overview of probabilistic methods
- Ideal for students and professionals
- Extensive 944 pages of content
- Published by a reputable press
- Clear and thorough explanations
缺點
- Some technical concepts may require prior knowledge
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