What is forward propagation in AI?

Forward propagation in AI refers to the process of passing input data through a neural network to generate an output. It involves performing a series of mathematical operations in each layer of the network, starting from the input layer and ending at the output layer. The result is a prediction that can be used for tasks like classification or regression.

How does forward propagation work?

Forward propagation works by applying a series of linear and non-linear transformations to the input data. In each layer, the data is multiplied by a set of weights, and a bias is added. The result is then passed through a non-linear activation function, which introduces non-linearity into the model and allows it to learn complex patterns.

The process is repeated for each layer in the network until the output layer is reached, which produces the final prediction.

What is the role of forward propagation in training a neural network?

Forward propagation plays a crucial role in training a neural network. It is the first step in the training process, producing a prediction that is compared with the true output to calculate the error of the network. This error is then used in the subsequent backpropagation step to adjust the weights of the network and improve its performance.