Neural network weighting

Question

I recently studied a network of backpropagation and did some manual exercises. After that, I had a question (maybe it doesn’t make sense): is there anything important in the following two different replacement methods: 1. Incremental training: the scales are updated immediately, as soon as all delta-widgets are known and before presenting the next training vector. 2. Batch training: Wij deltas are computed and saved for each training vector example. However, the Wij delta is not used immediately to update the weights. Weight updating is done at the end of the school age.

I searched Google for a long time but did not find any results.

Answer 1

So, you mean two modes for teaching gradient descents. In batch mode, changes in the weight matrix are accumulated throughout the entire presentation of the training data set (one “era”); Online training updates the weight after presenting each vector containing a set of workouts.

I believe the consensus is that online learning is excellent because it converges much faster (most studies lack obvious differences in accuracy). (See, for example, Randall Wilson and Tony Martinez, “General Inefficiency of Batch Learning for Learning Gradient Descent,” “Neural Networks” (2003).

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Answer 2

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