T. Scott Clendaniel on LinkedIn: #AI #ArtificialIntelligence #MachineLearning

In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML theory. The hyper-parameters are knobs that you tweak during each iteration of training a model to improve the accuracy in the predictions made by the model. The hyper-parameters are variables that govern the training process itself.