Discussion
Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.
Answer: B
Back propagation adjusts the weights by propagating the weight changes backward from the sink to the source. The error found at the output layer is passed back through the hidden layers and used to correct the weights of each layer.
Forward propagation passes the inputs through the network from the source to the sink to find the output, and does not change the weights. The option 'Backward from since to hidden nodes' and the option 'Forward from source to hidden nodes' do not describe the full path of the error, which goes from the output right back to the input.
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