l1 refers to a type of regularization technique known as Lasso (Least Absolute Shrinkage and Selection Operator) that is commonly used in machine learning, particularly with multilayer perceptrons and deep feedforward networks. It helps in preventing overfitting by adding a penalty to the loss function that is proportional to the absolute value of the coefficients of the model. This encourages sparsity in the model parameters, which means that some weights may become exactly zero, effectively selecting a simpler model.
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