Internal Parameters
Internal parameters refer to the variables or coefficients that are learned by a machine learning algorithm during the training process. These parameters are used to make predictions on new data. The values of internal parameters are updated iteratively during the training process until the algorithm converges to a set of optimal values. The optimal values of internal parameters are determined by minimizing a loss function that measures the difference between the predicted output and the actual output. The choice of optimization algorithm and the initial values of internal parameters can have a significant impact on the performance of the machine learning model. Internal parameters are specific to each machine learning algorithm and can include weights, biases, learning rates, regularization parameters, and activation functions.
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