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Sparsh Dutta
Sparsh Dutta

Sparsh Dutta

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From How to code Gaussian Mixture Models from scratch in Python by Thalles Silva

GMMs are a family of generative parametric unsupervised models that attempt to cluster data using Gaussian distributions.

From How to code Gaussian Mixture Models from scratch in Python by Thalles Silva

For high-dimensional data (D>1), only a few things change. Instead of estimating the mean and variance for each Gaussian, now we estimate the mean and the covariance. The covariance is a squared matrix of shape (D, D) — where D represents the data dimensionality. Be…

From How to code Gaussian Mixture Models from scratch in Python by Thalles Silva

Here, for each cluster, we update the mean (μₖ), variance (σ₂²), and the scaling parameters Φₖ. To update the mean, note that we weight each observation using the conditional probabilities bₖ.

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Build pipelines with Pandas using “pdpipe”

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[Notes] SHAP Values

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