A feedforward neural network is the simplest and most common type of neural network. It is composed of one or more layers of nodes, where each node takes inputs from the previous layer and produces outputs that are passed on to the next layer.
The network is called “feedforward” because the information flows in only one direction, from input to output. The input layer receives the input data, and the output layer produces the output or prediction. The hidden layers in between the input and output layers process the data by applying weighted sums and activation functions to the inputs.
Feedforward neural networks are typically used for supervised learning tasks such as classification and regression. During training, the network adjusts the weights of the connections between nodes in order to minimize the error between the predicted output and the actual output. This process is known as backpropagation.
One of the limitations of feedforward neural networks is that they are unable to handle sequential or time-series data. This is because the network does not have any memory of previous inputs or outputs, and cannot make predictions based on the order of the data.
Despite this limitation, feedforward neural networks are still widely used for a variety of applications, including image and speech recognition, natural language processing, and financial prediction. They are a powerful tool for solving complex problems and making accurate predictions based on large datasets.