Activation functions specify those curves, and the question is which curve gives the best fit? A linear function would fit the data only to a straight line, whereas nonlinear functions can capture ...

Activation functions play a vital role in the MLP's ability to model non-linear relationships within the data. Non-linear activation functions, such as the widely used sigmoid or rectified linear ...

Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what mathematicians call linear differential equations. But in nonlinear ...

Each neuron learns a limited function: f(.) = g(W*X) where W is the weight vector to be learned, X is the input vector and g(.) is a non-linear transformation. W*X can be visualized as a line ...