Bias Vector

What is Bias Vector?

A bias vector is an additional set of weights in a neural network that require no input, and this it corresponds to the output of an artificial neural network when it has zero input.

Bias represents an extra neuron included with each pre-output layer and stores the value of “1,” for each action. Bias units aren’t tied to any previous layer in the network, so they don't represent any form of activity, but are treated the same as any other weight.

Why are Bias Vectors Used?

Bias is a fundamental aspect of most machine learning techniques for several key reasons:

  • Without a bias node, no layer would be able to produce an output for the next layer that differs from 0 if the feature values were 0.
  • Bias nodes help networks solve more types of problems by allowing them to employ more complex logic gates.
  • Bias serves as the execution of a threshold value, making Perceptron and similar deep learning techniques possible.