Today I learned


  • Initially, when you start training, accuracy continues to go up. However, the model will start memorizing after sometime and validation set accuracy will decrease. That is when the model starts to overfit.
  • The model looks at all images exactly once in every epoch.
  • Architecture (for e.g., ResNet) is a template for a mathematical function. When trained, it turns into a model which contains parameters, possibly in millions, appropriate to tackle the problem at hand.
  • Train = fit.