Is the bias neuron of a neural network equivalent to a y-intercept of a linear regression?

Question

Is the above statement true? bias and \beta_0 are both weights that are independent of the input so the allow the model to add some constant value.

Answer 1

Firstly, a linear regression tries to estimate a function while a single neuron divides the input space into two sub-spaces, so they do essentially different tasks.

Having said that, the \beta_0 in neuron and the y-interception in linear regression are both biases: constants that are applied to the final output regardless of the output (just for clarification: they depend on the input in the sense that models learns them from input data).