November 2019
Intermediate to advanced
296 pages
7h 52m
English
Content preview from Hands-On Machine Learning with TensorFlow.js
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Centroid
The representative point of a cluster is called a centroid. This is the center of the samples that belong to the cluster and works as a prototype of the cluster. Therefore, finding the appropriate centroids that partition samples in a good manner is the goal of the K-means algorithm:
The centroid can be calculated as the mean of every point that belongs to the cluster. Assuming the sample points in the dataset are N vectors expressed as 
, the centroid of the cluster, , can be represented as follows:
This is just an equally weighted ...
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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.