bang bang !!

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hey guyyyyys ! sigmoid neurons are cool as heck man, although i do still have to figure out gradient descent and backpropagation but uh still so cool in my head. sigmoid neurons seem to be the qbit of the "digital neuron" world and i think that's why they so quickly obliterated perceptrons. i am brought back to my time working with the ibm quantum computers over summer for the quantum computing camp. i am rather certain that sigmoid neurons are of more use in current state though as qbits are still being researched.

coming into this silly litlle post i didn't plan on making the qbit comparison but i'll do my best to explain why the two (sigmoid neurons & qbits) line up in my head. as you might recall from my perceptron post (or maybe not 🙂‍↕️), computer neurons take inputs, that are then weighed, those weighted inputs must then surpass a certain bias that the perceptron holds. if the inputs do overcome the bias, the perceptron outputs '1', otherwise a '0' is spat out. for sigmoid neurons, the output (and subsequently the weights and biases but we don't wanna think abt that rn) is the largest difference. similarly to how bits are either '0' or '1', qbits and sigmoid neurons can be any number between '0' and '1' (inclusive).

(you guys r not getting a diagram this time)

big stuff is in the works tho! i'm working on a sigmoid proof of concept and then its 110% on this next project 🙂‍↕️😈

OOH YEAH I FORGOT TO SHARE A QUOTE IN PP BUT HERE HE IS: "passion is the genesis of genius" -uh idk BUT IT'S SO REAL AND I WAS GONNA TIE IT TO PERCEPTRONS OF PURPOSE but i forgot oops.

drake a whole goofy he drops 3 albums and goes 0 for 3 but i can't say i'm surprised 🤓

g'day