Neural networks are a type of artificial intelligence algorithm that are modeled after the structure and function of the human brain. They are composed of interconnected nodes or neurons, which process and transmit information in a way that is similar to the way the neurons in the human brain function.
In a neural network, input data is fed into the input layer, which is then processed by a series of hidden layers. Each neuron in the hidden layers is connected to other neurons through weighted connections, which determine the strength of the signal transmitted between them. The output of the final layer represents the output or prediction of the neural network.
During the training process, the weights between the neurons are adjusted based on the error or difference between the predicted output and the actual output. This process, called backpropagation, allows the neural network to learn and improve its accuracy over time.
Neural networks are commonly used in a wide range of applications, including image recognition, natural language processing, and predictive analytics. They are especially useful in situations where traditional algorithms may struggle, such as in cases where the input data is highly complex or the relationship between the input and output is not well understood.