What is the difference between back-propagation and feed-forward Neural Network?

machine-learning neural-network classification backpropagation
Unmesha SreeVeni picture Unmesha SreeVeni · Feb 9, 2015 · Viewed 43.4k times · Source

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

  1. Any other difference other than the direction of flow? What about the weight calculation? The outcome?
  2. Say I am implementing back-propagation, i.e. it contains forward and backward flow. So is back-propagation enough for showing feed-forward?

Answer

runDOSrun picture runDOSrun · Feb 9, 2015

  • A Feed-Forward Neural Network is a type of Neural Network architecture where the connections are "fed forward", i.e. do not form cycles (like in recurrent nets).

  • The term "Feed forward" is also used when you input something at the input layer and it travels from input to hidden and from hidden to output layer.
    The values are "fed forward".

Both of these uses of the phrase "feed forward" are in a context that has nothing to do with training per se.

  • Backpropagation is a training algorithm consisting of 2 steps: 1) Feed forward the values 2) calculate the error and propagate it back to the earlier layers. So to be precise, forward-propagation is part of the backpropagation algorithm but comes before back-propagating.

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