Answer Posted / Manish Dubey

A parameter in machine learning refers to variables that are learned from data during the training process. They are internal variables of an algorithm which are optimized or adjusted through the training procedure, such as weights and biases in a neural network. On the other hand, hyperparameters are external variables set before the training begins. They control the overall structure and behavior of the model and do not change during the learning process, examples include learning rate, batch size, and number of layers.

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