What does Non-Linearity mean in Neural Networks? -
A neural network is made of layers, and layers are made of nodes (also called neurons or units). It is these nodes that process input data. First, each neuron that receives input. If this is the first layer, the input is raw data. If it is a higher order layer, the input comes from the layer below. The node applies weights to the inputs. Then adds a bias term. Then applies a nonlinear activation function (such as ReLU or sigmoid) and produces its outputs the resulting value to the next layer, which receives it as input. The keyword here is “nonlinear” because in real life things are rarely linear. Everything […] Read More