What is ONE HOT ENCODING?
An efficient method of encoding the classes to train a network.
One Hot Encoding
[1,0,0]: Class A [0,1,0]: Class B [0,0,1]: Class C
0: Class A 1: Class B 2: Class C
In neural networks when we need to pick a class from classes, we have output nodes equal to the number of classes. Each node shows the probability that it may matches Class A, Class B or Class C. It may look like this: Class A has probability equal to 0.1 Class B has probability equal to 0.2 Class C has probability equal to 0.7
Although it looks inefficient from the storage perspective, but it is very efficient for the training. Moreover, it complements argmax function, that saves us writing a lot of code.