January 2024
Intermediate to advanced
408 pages
13h 21m
English
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Chapter 1: Machine learning cloud regression and optimization
The Swiss army knife of optimization
Abstract
This chapter is not about regression performed in the cloud. It is about considering your data set as a cloud of points or observations, where the concepts of dependent and independent variables (the response and the features) are blurred. It is a very general type of regression, offering backward-compatibility with existing methods. Treating a variable as the response amounts to setting a constraint on the multivariate parameter, and results in an optimization algorithm with Lagrange multipliers. The originality comes from unifying and bringing under the same umbrella a number of disparate methods, each solving a part of the general problem ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.