This is extremely interesting. In the forecast domains I work, (I use neural networks on standardized inputs) I typically use 50-100 epochs at most for a few th

This is extremely interesting. In the forecast domains I work, (I use neural networks on standardized inputs) I typically use 50-100 epochs at most for a few thousand rows (with some approx 10 input neurons) and I don't see networks improving very much after that. However, our industry peers often use a few thousand up to several ten thousands of epochs, yet our software is regularly best or second best when compared by clients. I think this may be the same effect: Network weights should be "ok" after a few dozen epochs and any training beyond that maybe wins a percent of variance explained but already risks overfitting. We use the spare time to train more networks (on slightly different input data) whose results we aggregate after that. Something else: can someone explain the "I don't make no nevermind", I suppose it's funny but I don't get it.