This tutorial is quite impressive!
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.