Map&Models - Lecture 08- Self Organizing Maps

In this lecture, in the theory part, we give a concise explanation of what is a Self Organizing Map algorithm (SOM), how it works, and how to use it. We continue by introducing some examples with different modalities of data. And last we show how important is the encoding (numerical vectors) of the data to have a final result with SOM -emphasizing that depending on your interest, the SOM organizes the data.
In the coding part, we build from scratch a Self Organizing Map algorithm and show one application with images. We continue with the implementation of SOM in our experiment. Here we explained step by step the code, to finally achieve a SOM with your crawled data.

[@lindaflow5434]

thanks for sharing this lecture! :)

[@MuhammadEbrahim9602]

Where to access code?

[@possoliessowe8139]

pouvez vous nous envoyer le code final?

[@Jkauppa]

whats this different from pca or linear regression, more like pca

[@Jkauppa]

are you trying to project the data to a lower dimension space, like 2d, with space warping clustering

[@Jkauppa]

you are solving a data compression pca algorithm

[@Jkauppa]

you did not do anything