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- Mar 31, 2023

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This calculation is used to find the two parameters of a neuron F = ae + bf without bias. It's not really practical because training it on a huge dataset would be inconceivable. The calculation demonstrated below is designed for a neuron with two inputs, one output, two parameters, and only two data points. Here's an example of usage: {'inputs': [1, 2], 'targets': 0}, {'inputs': [3, 4], 'targets': 1}, where a = 1, b = 2, c = 3, d = 4, g = 0, and h = 1.

I am looking to create an iterative version of this calculation that would allow us to train our neurons with as much data, inputs, outputs, and hidden layer neurons as we want.

Thanks to everyone reading this message and sharing some of their ideas!

Phro0244

PS: I did this calculation myself; you won't find anything like it online.