Hyperparameters are parameters used to set up and control the learning process of an ML model. They are applied before the actual training. Hyperparameters define the properties of an ML algorithm, for example, learning rate. Fine-tuning such parameters helps optimize the model training.
In contrast to hyperparameters, parameters are the weights and biases that an ML model learns during training
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