{"@attributes":{"version":"2.0"},"channel":{"title":"Project on Oriol Al\u00e0s Cerc\u00f3s","link":"https:\/\/oriolac.github.io\/tags\/project\/","description":"Recent content in Project on Oriol Al\u00e0s Cerc\u00f3s","generator":"Hugo -- 0.150.0","language":"en-us","copyright":"Oriol Al\u00e0s Cerc\u00f3s","lastBuildDate":"Sun, 21 Dec 2025 11:10:23 +0100","item":{"title":"Variational AutoEncoders (VAE) for Tabular Data","link":"https:\/\/oriolac.github.io\/posts\/20251210-vae-tabular\/","pubDate":"Sun, 21 Dec 2025 11:10:23 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20251210-vae-tabular\/","description":"<p>The post of today is going to be a bit different. We have already talked about <strong>Variational Autoencoders (VAE)<\/strong>\n<a href=\"http:\/\/oriolac.github.io\/posts\/20250710-starting-diffusion\/\" target=\"_blank\" rel=\"noopener\">in the past<\/a>, but today we are going to see how to\nimplement it from scratch, train it on a dataset and see how it behaves with <strong>tabular data<\/strong>. Yes, VAEs can be used for\ntabular data as well. To do so, we will use the <strong>CRISP-DM framework<\/strong> to guide us through the process.<\/p>"}}}