Lecture-50: ‘Model Parameters’ and ‘Hyperparameters’ in ML & DL? (Theory)
In This video i was explained about 'Model parameters' & 'Hyperparameters' in ML & DL.
Model Parameters:
While training the model, parameters are continuously being updated and the final ones at the end of the training constitute your model.
For example, ‘W’ Weights or Coefficients of independent variables in the Linear regression model.
Hyperparameters:
Hyperparameters are those parameters that are explicitly defined by the user to control the learning process.
These are usually defined manually by the machine learning engineer.
One cannot know the exact best value for hyperparameters for the given problem.
The best value can be determined either by the rule of thumb or by trial and error.
These are adjustable parameters that must be tuned in order to obtain a model with optimal performance.
you set the hyperparameters before training begins and the learning algorithm uses them to learn the parameters.
Therefore, setting the right hyperparameter values is very important because it directly impacts the performance of the model that will result from them being used during model training.
The process of choosing the best hyperparameters for your model is called hyperparameter tuning.
For example, the learning rate in DL.