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planspace.org | ||
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teddykoker.com
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| | | | | A few posts back I wrote about a common parameter optimization method known as Gradient Ascent. In this post we will see how a similar method can be used to create a model that can classify data. This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. Lets start by importing all the libraries we need: | |
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sportscidata.com
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| | | | | Recently Dan Weaving and the research group at Leeds Beckett University put out a paper outlining how to perform a type of dimension reduction on training load data: principal component analysis (PCA). The benefit of such an analysis is it can reduce a large number of metrics into a more manageable dataset. This may uncover... | |
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echarts4r.john-coene.com
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| | | | | [AI summary] A technical article guide demonstrates how to use a function in R to plot points, lines, and areas on geospatial style charts for data visualization. | |
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rpsychologist.com
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