January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Kernel
A non-separable dataset like the one used previously is always a tough thing to deal with, however, there are ways to deal with it. One way is to set the vectors into higher dimensions through transformation. But, can we really do it when we have millions of data or vector in reckoning? It will take lots of computation and, also, time. That's where kernel to saves our day.
We have seen the following equation. In this, only the dot product of the training examples are responsible for making the model learn. Let's try to do a small exercise here:
Let's take two vectors here:
x1=[4,8] x2= [20,30]
Now, build a transformation function that ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.