Question

What is the difference between back-propagation and feed-forward neural networks? By googling and reading, I found that in feed-forward there is only forward direction, but in back-propagation, once we need to do a forward-propagation and then back-propagation. I referred to this link Is any other difference other than the direction of flow? What about the weight calculation? The outcome? Say I am implementing back-propagation, i.e. it contains forward and backward flow. So is back-propagation enough for showing feed-forward? Select the correct answer from above options

Answer 1

Feed-forward neural networks: The signals in a feedforward network flow in one direction, from input, through successive hidden layers, to the output. The connections between the nodes do not form a cycle as such, it is different from recurrent neural networks. Backpropagation is a training algorithm consisting of 2 steps: Feedforward the values. Calculate the error and propagate it back to the earlier layers. Forward-propagation is a part of the backpropagation algorithm but comes before back-propagating the signals from the nodes. The basic type of neural network is a multi-layer perceptron, which is a Feed-forward backpropagation neural network. Hope this answer helps.