Max Goes To The Barber Bridge Learning


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Max Goes to the Barber [Bridge Learning]


Max Goes to the Barber [Bridge Learning]

Author: Adria F Klein

language: en

Publisher:

Release Date: 2009-01-01


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During a visit to the barber, Max gets his hair cut and combed.

Powering a Learning Society During an Age of Disruption


Powering a Learning Society During an Age of Disruption

Author: Sungsup Ra

language: en

Publisher: Springer Nature

Release Date: 2021-05-22


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This open access book presents contemporary perspectives on the role of a learning society from the lens of leading practitioners, experts from universities, governments, and industry leaders. The think pieces argue for a learning society as a major driver of change with far-reaching influence on learning to serve the needs of economies and societies. The book is a testimonial to the importance of ‘learning communities.’ It highlights the pivotal role that can be played by non-traditional actors such as city and urban planners, citizens, transport professionals, and technology companies. This collection seeks to contribute to the discourse on strengthening the fabric of a learning society crucial for future economic and social development, particularly in the aftermath of the coronavirus disease.

Mathematics for Machine Learning


Mathematics for Machine Learning

Author: Marc Peter Deisenroth

language: en

Publisher: Cambridge University Press

Release Date: 2020-04-23


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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.