Parameters vs hyperparameters in machine learning

In this short video we will discuss the difference between parameters vs hyperparameters in machine learning. Parameters is something that a machine learning model trains and figure out such as weights and bias for the model. Hyper parameter on the other end is something you manually specify such as number of hidden layers, neurons in each hidden layer, epochs etc.

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❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.