How are Neural Networks modelled?

Neural networks are mathematical models inspired by the structure and function of biological neural networks. They consist of layers of interconnected nodes or artificial neurons that are trained by adjusting the weights and biases of the connections between them. The most common type is the feedforward neural network, which uses backpropagation to adjust the weights and biases during training. Other types include recurrent neural networks and convolutional neural networks. They are used for various applications, from speech recognition to natural language processing.

 A simple neural network includes an input layer, an output (or target) layer and, in between, a hidden layer. The layers are connected via nodes, and these connections form a “network” – the neural network – of interconnected nodes.