kernel_regularizer=regularizers.l2(penalty)

This line of code specifies the L2 regularization penalty for the kernel weights in a neural network. Regularization is a technique used to prevent overfitting, which occurs when a model becomes too complex and can fit the training data too well, leading to poor performance on new, unseen data. L2 regularization adds a penalty term to the loss function being optimized that penalizes large weights, encouraging the model to use smaller weights and reducing overfitting. The `penalty` parameter specifies the strength of the regularization, with larger values leading to stronger regularization. The `kernel_regularizer` parameter is typically set when defining the layers of a neural network using a framework like Keras or TensorFlow.