How to systematically choose the architecture of a neural network (NN) for a concrete task?
For example, I am solving classification task with 3 classes (NN should recognize pandas, dogs and cats). What type of architecture I should choose? Is there a general rule of thumb?
So far, I have simply picked an architecture, which has 90% accuracy on CIFAR-10 dataset, and been trying to apply it to my task. Results are not very good (on 30 epoch, I got only about 40% accuracy on train set and 57% on val set).