Loading subject…
Activation function
The function a node applies to its weighted sum to produce its output. It introduces non-linearity, which is what allows a network of layers to model more than a single straight boundary.
Artificial Neural Network (ANN)
A computational model built from interconnected nodes, or neurons, loosely inspired by the brain.
Backpropagation
The training step that works backwards from the loss at the output layer, calculating how much each weight and bias contributed to the error so gradient descent can adjust them.
Bias (neural network)
A learned constant added to a node's weighted sum, which shifts the threshold at which the node fires so its decision boundary need not pass through the origin.
Perceptron
The simplest neural network: a single node that multiplies each input by a weight, adds a bias, and passes the total through an activation function to produce one output.