Here are some key points about backpropagation and forward propagation:
Forward Propagation:
- Forward propagation is the process of computing the output of a neural network given an input.
- It involves passing the input through the layers of the network, computing the weighted sum of the inputs to each neuron, applying a nonlinear activation function to the result, and passing the output to the next layer.
- The output of the last layer is the output of the network.
Backpropagation:
- Backpropagation is an algorithm used to train neural networks by computing the gradients of the loss function with respect to the weights of the network.
- It involves computing the output of the network for a given input using forward propagation, computing the error between the output and the target, and propagating the error backwards through the network to compute the gradients of the loss function with respect to the weights.
- The gradients are used to update the weights of the network using an optimization algorithm such as stochastic gradient descent.
- Backpropagation involves the chain rule of calculus to compute the gradients of the loss function with respect to each weight in the network.
Overall, forward propagation is used to compute the output of a neural network given an input, while backpropagation is used to train the network by computing the gradients of the loss function with respect to the weights of the network.