Hi Andrew,
Share a question I posted about bias. Hope that can clarify the purpose of a bia.
And Conwyn gives a very interesting example, it’s similar with my understanding while learning the lessons. Multiplying weights by pixels and sum them up is for constructing a “equation (set)”, but we don’t knonw the parameters(that is wights) of every variable and the constant(that is bias) . So we initiate a group of random parameter and random constant. And fill solutions(that is train data set, in the MNIST example, it is pixels of a image) into the equation and check if the “equation” is perfect enough, if not, adjust the parameters and then fill solutions, and check again …
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