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 ...

For this, you also need non-linear thresholding functions that adjust the weights between the layers of neurons. “What makes deep neural networks so powerful is that we’re able to map very ...

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 ...