does "separating negation pairs" entirely distinguish linear vs nonlinear boolean functions??

hand programming mlps!

implementing a cool idea for small binary nn visualizer p3

implementing a cool idea for small binary nn visualizer p2

implementing a cool idea for small binary nn visualizer

hand setting neural net weights

decomposing small nns for an hour

prgrm nn maybe

if you try to run a small neural net backwards by hand

making small mlps wo gradscent and then compare with gradscent pt 2 (hot gpt4o action)

making small mlps wo gradscent and then compare with

trying to grok 4-bit circuits/mlps

implication structure of ltfs with 0 bias and sorted weights

implementing new circuits experiment

if you have boolean circuit with only majority and inputs, what it is that like

ltfs with 0 bias, positive sorted weights, how different can they be?

linear threshold functions are always 37.5% correlated with an input??

linear threshold functions are always 37.5% correlated with an input??

linear threshold functions are always 37.5% correlated with an input

mumblecode - largely incoherent debugging and then cool vis of nonlinearities in boolean functions

mumblecode - why closest linear functions to random function all flip same input??

Nonlinear boolean functions and their distance to the set of linear boolean functions

neural nets HATE this simple 3d geometric transform

does "separating negation pairs" entirely distinguish linear vs nonlinear boolean functions??

Making a small neural net chase a larger neural net