MLG 032 Cartesian Similarity Metrics
Machine Learning Guide
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Norms, Normed Distances, Euclid Similarity
In a recent machine learning applied episode, i talked about how you can use the natural language processing tool hugging face transformers to convert your documents into an embedded ve. We would make those comparisons using metrics discussed in this episode. Specifically, we use cosign similarity, and we will talk about that in a bit. Another option one might consider is to use euclidian similarity. And that might play in either in comparing document to document, or in clustering doc s using the k means clustering. So let's get started.
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