Question: You want to solve a classification task. You first train your network on 2 0 samples. Training converges, but the training loss is very high.

You want to solve a classification task. You first train your network on 20
samples. Training converges, but the training loss is very high. You then
decide to train this network on 10,000 examples. Is your approach to fixing
the problem correct? If yes, explain the most likely results of training with
10,000 examples. If not, give a solution to this problem.

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