[PDF] Real Cause Of The High Price Of Gold Bullion 2d Ed Corr With An Appendix eBook

Author : Raymond Serway
Publisher : Cengage Learning
Page : 1344 pages
File Size : 36,67 MB
Release : 2013-01-01
Category : Science
ISBN : 9781133953951

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As a market leader, PHYSICS FOR SCIENTISTS AND ENGINEERS is one of the most powerful brands in the physics market. While preserving concise language, state-of-the-art educational pedagogy, and top-notch worked examples, the Ninth Edition highlights the Analysis Model approach to problem-solving, including brand-new Analysis Model Tutorials, written by text co-author John Jewett, and available in Enhanced WebAssign. The Analysis Model approach lays out a standard set of situations that appear in most physics problems, and serves as a bridge to help students identify the correct fundamental principle--and then the equation--to utilize in solving that problem. The unified art program and the carefully thought out problem sets also enhance the thoughtful instruction for which Raymond A. Serway and John W. Jewett, Jr. earned their reputations. The Ninth Edition of PHYSICS FOR SCIENTISTS AND ENGINEERS continues to be accompanied by Enhanced WebAssign in the most integrated text-technology offering available today. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 29,56 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.