Status Updates From Why Machines Learn: The Ele...

Lots of math and proofs on finding a hyperplane and shifting the weights. Algorithm on p. 51. Talks about setting upper and lower bounds for determining how many times we should re-calculate, since for complex problems you could run forever and ever searching for an answer

Dot products and matrix multiplication, which honestly lost me.

P. 46 has an important equation and diagrams for understanding all that connection to the Perceptron, which makes some sense at least. The vector for weight, w, will always have a perpendicular line to itself (itself being hyperplane) that is what classifies this outputs as -1 or 1.

Vectors (magnitude and direction) and scalars (quantity, just a number)

Def learned this before in Physics or calc in college. It clicked again on pp. 29-30 with the graphs. The Cartesian coordinates we def used before on p. 33. Vector math is super simple too, see p. 32.

End chapter 1. Perceptrons and their ways of clumping data together to find separations (given they exist), linearly. The graph with a lime separating all triangles on the left and circles on the right of the line shows a basic example of this (p. 23)