A Neural Network is a computational structure that was inspired by the human brain. It is designed to recognize patterns and make predictions based on data.
The basic building block of a neural network is the neuron, also known as a node. Each neuron accepts inputs, which are multiplied by weights and added together with a bias value. The result is then passed through an activation function to produce the neuron’s output.
Neurons are organized into layers, with the outputs of one layer serving as the inputs to the next layer. The connections between neurons contain weights, which determine the strength of the connection. A weight of zero means that there is no connection between the neurons, while a weight of 1 means that there is a strong connection. Decimal values can be used to represent intermediate connection strengths.
During training, a neural network is presented with example data and adjusts its weights in response. This allows the network to learn from the data and make accurate predictions when presented with new data.
Neural networks have many applications, including image recognition, natural language processing, and predictive modelling. They are a powerful tool for solving complex problems and have become increasingly popular in recent years.
