Hello everyone, Today I’m sharing about artificial neural networks.

A class of machine learning models known as artificial neural networks (ANNs) is motivated by the way biological neural networks, like the human brain, process information. They are applied to a variety of machine learning and artificial intelligence tasks.

Important artificial neural network constituents consist of:

Neurons (Nodes): An artificial neural network’s neurons are comparable to the brain’s neurons. Every neuron receives and processes one or more inputs before producing an output. Activation functions are typically used to transform these outputs.

Layers: There are layers in an ANN. There are three primary kinds of layers:

The input layer is where the first features or data to be processed are received.
Hidden Layers: The input data is subjected to intricate changes by these intermediate layers. There are several hidden layers in deep networks.
The output layer generates the ultimate result or forecast for the task assigned to the network.
Weights: Every neuronal connection has a corresponding weight. The strength of the connection between neurons is determined by these weights.