Model Weights

In the context of data science and artificial intelligence, model weights refer to the numerical values assigned to the parameters of a machine learning model during the training process. These weights determine the strength and direction of the connections between the neurons in the model, and are adjusted iteratively during training to minimize the error between the predicted output and the actual output. The weights are typically initialized randomly and updated using optimization algorithms such as stochastic gradient descent. The final set of weights represents the learned knowledge of the model, and can be used to make predictions on new data. Model weights are often stored separately from the model architecture, allowing for easy transfer and reuse of the learned knowledge.
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