Trained Model Parameters

Trained model parameters refer to the weights and biases that are learned by a machine learning algorithm during the training process. These parameters are adjusted by the algorithm to minimize the difference between the predicted output and the actual output. The process of adjusting these parameters is known as optimization. The trained model parameters are used to make predictions on new data. The accuracy of the predictions depends on the quality of the trained model parameters. In deep learning, the number of trained model parameters can be very large, sometimes in the millions or billions, which requires significant computational resources to train the model.
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