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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
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The backpropagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.
Technically, the backpropagation algorithm is a method for training the weights in a multilayer feed-forward neural network. As such, it requires a network structure to be defined of one or more layers where one layer is fully connected to the next layer.
A convolutional neural net is a structured neural net where the first several layers are sparsely connected in order to process information (usually visual). A feed forward network is defined as having no cycles contained within it. If it has cycles, it is a recurrent neural network.
A feedforward neural network is a type of artificial neural network in which nodes' connections do not form a loop. Often referred to as a multi-layered network of neurons, feedforward neural networks are so named because all information flows in a forward manner only.
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.
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