Asked about Reading training curves
Your validation loss starts rising while training loss keeps falling. What is happening and what do you do?
An answer that works
The model is overfitting: memorising training examples rather than learning patterns that generalise. Stop at the best validation point, then add regularisation such as weight decay, dropout or augmentation, get more data, or reduce model size.
Then they push further
Could it be anything other than overfitting?
Yes. The validation set might differ from training data, there could be leakage making training easier than it should be, or the learning rate schedule might be misbehaving. Check the validation data and a few predictions before assuming.
Reading an answer is not being able to give one. The section that teaches this is Reading training curves, 3 hours of Neural networks.
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