I have solved quite a few kaggle playground problems lately, but I can't understand how to come up with good enough model architecture which gets 0.9+ validation accuracy and without overfitting.
Is there some formula, or is it some hit and trial method for determining filters and neurons. And also I'm always using relu activation in hidden layers(read it somewhere) when will other activations be used in hidden ones.
Can you lay down some guidelines that I should follow?. Considering I mostly work with image classification problems.
And I you have some other tips as well you can share in your answer.