Coffee roasting lab: np.tile purpose

There is this part in the lab

Tile/copy our data to increase the training set size and reduce the number of training epochs.

Xt = np.tile(Xn,(1000,1))
Yt= np.tile(Y,(1000,1))

Can someone point me to the explanation of this step, why we need to do this?

I’m sure if it is already discussed in the video, but i can’t remember it being discussed.

Googling it doesn’t give me result as well.


This technique is called “data augmentation,” very common in ML. With these additional samples, the model can generalizable patterns more accurately, which can improve its performance.


Thanks @saifkhanengr for the reply, very appreciated it! :pray: