50+ days of Machine Learning

 Hello :) Today is Day 117! A quick summary of today: watched a couple of 3brown1blue 's vides transferred more posts to the new blog Firstly, I watched this one about abstract vector spaces In abstract vector spaces we are dealing with a space that exists independently from the coordinates we are given. and the coordinates can be arbitrary depending what we choose as our basis vector. Determinants and eigenvectors dont care about the coordinate system - the determinant tells us how a transformation sclaes areas, and eigenvectors are the ones that stay on their own span during a transformation. Se can change the coordinate system, and it wont affect the values of the above 2. Functions also have vector-ish qualities. in the same we can add 2 vectors together, we can add 2 functions to get a third, resulting function. It is similar to adding vectors coordinate by coordinate. Similarly, we can scale a function. and it is the same as scaling a vector by a number What does it mean for