parameters_hyperparameters_difference

## Parameters and Hyperparameters!! Are they same??

No, they are different. Basically, hyperparameters are introduced when we are about to tune a model, be it on the training data set or the validation data set. These 
hyperparameters change as the models change.

For example, in:

>>> K means clustering : k i.e. the number of clusters is a hyperparameter

>>> Random Forest : Number of layers is a hyperparameter

>>> Decision Tree : Depth of the tree is a hyperparameter


Slowly, we may understand what values of hyperparameters will be more useful than the others with more trial and error.

It is best explained by Andrew Ng, please do check out!!

https://www.youtube.com/watch?v=VTE2KlfoO3Q&ab_channel=Deeplearning.ai