Description: Probabilistic Machine Learning by Kevin P. Murphy Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.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 authors 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. Author Biography 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. Details ISBN 0262046822 ISBN-13 9780262046824 Title Probabilistic Machine Learning Author Kevin P. Murphy Format Hardcover Year 2022 Pages 944 Publisher MIT Press Ltd GE_Item_ID:141685731; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 132.67 USD
Location: Fairfield, Ohio
End Time: 2024-11-15T01:13:42.000Z
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Restocking Fee: No
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Refund will be given as: Money Back
ISBN-13: 9780262046824
Book Title: Probabilistic Machine Learning
Number of Pages: 864 Pages
Language: English
Publication Name: Probabilistic Machine Learning : an Introduction
Publisher: MIT Press
Subject: Intelligence (Ai) & Semantics, Computer Science, General
Item Height: 1.5 in
Publication Year: 2022
Item Weight: 55.6 Oz
Type: Textbook
Item Length: 9.3 in
Author: Kevin P. Murphy
Subject Area: Computers, Science
Item Width: 8.3 in
Series: Adaptive Computation and Machine Learning Ser.
Format: Hardcover