How To Deal With Overfitting In Deep Learning Models
Say you are eating an apple for the first time and it turns out to be rotten. The first thought that comes to your mind is "apples are rotten". While this is not an accurate assumption, it is human nature to overgeneralize things due to the lack of a variety of experiences. When the same thing happens with a machine learning model, we call it "overfitting". A machine learning or deep learning model is said to be overfitted if it produces a high training accuracy but a low out-of-sample accuracy. Overfitting happens when the model is too complex relative to the amount and noisiness of the training data. An overfitted model becomes too accustomed to the training data that it fails to perform well on any data that is not similar or not a part of the training set. As a beginner in the field of Deep Learning, overfitting was one of the most troublesome things to deal with. So, I made of list of all the techniques I've used to deal with overfitting to make it a li...