Hi I'm new to deep learning and convolutional neural network. Could someone please explain the problem in the figure below? Someone told me that the fluctuation of validation accuracy is the problem here. But I don't quite understand the negative effect of this fluctuation. Why don't we just look at the last point of the figure?
When training a deep learning module you have to validate it. Which means you are showing the unseen data to algorithm.
So validation accuracy can be less that the training accuracy. Because there's an scenario called over-fitting. Where your training algorithm is too much attached to training data and does not generalize well to other unseen data.
On the fluctuating issue it can be normal. Because we training and testing the algorithm is a stochastic manner.
