Verkäufer
BestAroundDeals, Grand Rapids, MI, USA
Verkäuferbewertung 5 von 5 Sternen
AbeBooks-Verkäufer seit 4. Juni 2020
Bestandsnummer des Verkäufers ABE-1684586071843
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.
Über die Autorin bzw. den Autor:
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.
Titel: Probabilistic Machine Learning: An ...
Verlag: The MIT Press
Erscheinungsdatum: 2022
Einband: Hardcover
Zustand: New
Anbieter: Bellwetherbooks, McKeesport, PA, USA
hardcover. Zustand: Good. Bruise/tear to cover. Bestandsnummer des Verkäufers mon0000015742
Anzahl: 4 verfügbar
Anbieter: Bellwetherbooks, McKeesport, PA, USA
hardcover. Zustand: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting. Bestandsnummer des Verkäufers mon0000007682
Anzahl: 1 verfügbar
Anbieter: HPB-Red, Dallas, TX, USA
hardcover. Zustand: 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! Bestandsnummer des Verkäufers S_453083560
Anzahl: 1 verfügbar
Anbieter: ChristianBookbag / Beans Books, Inc., Westlake, OH, USA
hardcover. Zustand: New. New with remainder mark. Buy multiples from our store to save on shipping. Bestandsnummer des Verkäufers 2511120220
Anzahl: 1 verfügbar
Anbieter: ChristianBookbag / Beans Books, Inc., Westlake, OH, USA
hardcover. Zustand: Very Good. Scratch and dent. Cover may have wear, dings, tears, other damage, or be missing dust jacket. Buy multiples from our store to save on shipping. Bestandsnummer des Verkäufers 2512020342
Anzahl: 2 verfügbar
Anbieter: CollegePoint, Inc, Memphis, TN, USA
Hardcover. Zustand: Good. We only honor returns for quality issues and won't accept reasons such as 'change my mind', 'find a better price', or 'school book requirement change', etc. Bestandsnummer des Verkäufers 10043
Anzahl: 1 verfügbar
Anbieter: AMM Books, Gillingham, KENT, Vereinigtes Königreich
hardcover. Zustand: Good. In stock ready to dispatch from the UK. Bestandsnummer des Verkäufers mon0000282184
Anzahl: 1 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. pp. 864. Bestandsnummer des Verkäufers 26387805125
Anzahl: 3 verfügbar
Anbieter: Copperfield's Used and Rare Books, Petaluma, CA, USA
Hardcover. Zustand: Coll - U6 - Very Good. Hardcover, VG. Pages bright and clean. Minimal shelfwear. Bestandsnummer des Verkäufers 6208928
Anzahl: 1 verfügbar
Anbieter: Jadewalky Book Company, HANOVER PARK, IL, USA
Zustand: 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. Bestandsnummer des Verkäufers Y2-BZI8-O6YW
Anzahl: 3 verfügbar