Forward Propagation: The hidden layer, between the input layer and the output layer of the network, receives inputs with weights. We calculate the output of the activation at each node at each hidden layer, and this propagates to the next layer until we reach the final output layer. We go forward from the inputs to the final output layer, which is known as the forward propagation.
Back Propagation: It sends error information from the network's last layer to all of the weights within the network. It's a technique for fine-tuning the weights of a neural network based on the previous epoch's (i.e., iteration) error rate. By fine-tuning the weights, you may lower error rates and improve the model's generalization, making it more dependable. The process of backpropagation can be broken down into the following steps: It can generate output by propagating training data through the network. It, then, computes the error derivative for output activations using the target and output values. It can backpropagate to compute the derivative of the error in the previous layer's output activation, and so on for all hidden layers. It calculates the error derivative for weights using the previously obtained derivatives and all hidden layers. The weights are updated based on the error derivatives obtained from the next layer.