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This paper proposes several variations to the standard weighted sum formula used in machine learning models. These variations include regularization, bias terms, different activation functions, different optimization techniques, and attention mechanisms. We demonstrate how these variations can improve the performance of machine learning models by reducing overfitting, capturing more complex relationships in the data, learning more efficiently, and focusing on relevant features of the input data. We also provide experimental results on various datasets to support our claims. Our findings have practical implications for improving the accuracy and generalization performance of machine learning models in various fields.
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