What is Weight (Artificial Neural Network)?
Weight is the parameter within a neural network that transforms input data within the network's hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network. Often the weights of a neural network are contained within the hidden layers of the network.
How does Weight work?
It is helpful to imagine a theoretical neural network to understand how weights work. Within a neural network there's an input layer, that takes the input signals and passes them to the next layer.
Next, the neural network contains a series of hidden layers which apply transformations to the input data. It is within the nodes of the hidden layers that the weights are applied. For example, a single node may take the input data and multiply it by an assigned weight value, then add a bias before passing the data to the next layer. The final layer of the neural network is also known as the output layer. The output layer often tunes the inputs from the hidden layers to produces the desired numbers in a specified range.
Weight vs. Bias
Weights and bias are both learnable parameters inside the network. A teachable neural network will randomize both the weight and bias values before learning initially begins. As training continues, both parameters are adjusted toward the desired values and the correct output. The two parameters differ in the extent of their influence upon the input data. Simply, bias represents how far off the predictions are from their intended value. Biases make up the difference between the function's output and its intended output. A low bias suggest that the network is making more assumptions about the form of the output, whereas a high bias value makes less assumptions about the form of the output. Weights, on the other hand, can be thought of as the strength of the connection. Weight affects the amount of influence a change in the input will have upon the output. A low weight value will have no change on the input, and alternatively a larger weight value will more significantly change the output.