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Download or read book AA Maxi Scale France written by and published by Aa Pub. This book was released on 2004-11-30 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new 2005 edition of this large-scale French atlas is detailed, yet easy-to-use with a scale of 2.8 miles to 1 inch. Includes information on distances and journey times, map symbols, route planning, key to map pages and a Department map.

Download or read book AA Maxi Scale France 2004 written by Automobile Association (Great Britain) and published by . This book was released on 2003-12-31 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This atlas uses an extra large scale and clear digital mapping from the AA and IGN, France's national mapping agency. Features include: 83 clear town plans to aid navigation; maps of Paris and its environs; an easy-to-use, colour-coded index; details of tunnels, passes and pass heights; multi-lingual map symbols; a route-planner; a distance chart, journey times and key-to-map pages; and 36,500 symbols for places of interest.

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Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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