For those interested in dimensionality reduction

This tutorial is quite impressive!

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BTW, if like me you wonder what is an hyperparameter, I’m told that it is the parameter of the algorithm itself, like the learning rate in a neural net.

You can read more about this distinction here. It appears that the consensus is that a hyper parameter is a stricter definition of a parameter that is learned from the data as opposed to being defined by the user. In practice in ML circles I mostly see people mention hyper parameters and not worry so much about the distinction.


Awesome! I’m going to send this to my mum. She looked a bit sick last time I tried to explain to her what was going on.