Respuesta :
In an ANN, Connection weights express the relative strength (or mathematical value) of the input data or the many connections that transfer data from layer to layer.
What Do ANN Weights Mean?
The weight parameter in a neural network modifies input data in the network's hidden layers. A neural network is made up of a network of neurons or nodes. Each node consists of a set of inputs, a weight, and a bias value. When an input enters the node, it is multiplied by a weight value, and the output is then either seen or sent on to the neural network's subsequent layer. The weights of a neural network are usually found in the layers.
Visualizing a theoretical neural network might be helpful in understanding how weights function. A neural network's input layer accepts input signals and transmits them to the following layer.
The input data is then altered by a number of hidden layers in the neural network. The buried layers' nodes are where the weights are applied. For example, a single node might receive the incoming data, multiply it by a specified weight value, apply a bias, and then transfer the results to the layer below. It is also known as the output layer, the final layer of the neural network.
Therefore, the linkages between layers of data or their numerous connections are what indicate the relative strength (or mathematical worth) of the incoming data.
For more information on ANN, refer to the given link:
https://brainly.com/question/23824028
#SPJ4
