Question

Describe the structure of an Artificial Neural Network by explaining its three fundamental layers, and define the role of the weights assigned to each connection between the nodes.

Hint:

ANN structure = Input → Hidden → Output. Weights decide how strongly one neuron influences another.

Answer 1

Step 1: Define Artificial Neural Network (ANN).
An Artificial Neural Network (ANN) is a computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process information.

Step 2: Explain Input Layer.
The input layer is the first layer of the network that receives raw data from the external environment. Each neuron in this layer represents a feature of the input data.

Step 3: Explain Hidden Layer.
The hidden layer(s) lie between input and output layers and perform complex computations. These layers process the input using activation functions and extract patterns or relationships.

Step 4: Explain Output Layer.
The output layer produces the final result of the network, such as classification or prediction, based on the processed data.

Step 5: Role of Weights.
Weights are numerical values assigned to connections between neurons. They determine the importance of input signals. During training, these weights are adjusted to minimize error and improve accuracy.