Neural Networks

A neural network is a collection of interconnected processing nodes, or neurons, that work together to process information. Neural networks are similar to other machine learning algorithms, but they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data.

Neural networks are composed of input nodes, output nodes, and hidden nodes. Input nodes receive input data, output nodes produce the desired output, and hidden nodes transform the input data into the output.

Training a neural network involves adjusting the weights of the connections between the nodes so that the network can accurately produce the desired output for a given set of inputs. This process is typically done using a training set of data that includes the desired output for each input.

Once a neural network has been trained, it can be used to classify new data. This process of using a trained neural network to classify new data is called inference.

Neural networks are commonly used for tasks such as image recognition and classification, sequence prediction, and forecasting. They are also used in a variety of other fields such as medicine, finance, and engineering.