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In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
The BatchNorm module does not operate over individual inputs. Instead, it must operate over one batch of inputs at a time. Concretely, suppose we have a batch of inputs () (), (), …, (), fed all at once into the network. We would obtain in the middle of the network some vectors:
The form the population iteration, which converges to , but cannot be used in computation, while the form the sample iteration which usually converges to an overfitting solution. We want to control the difference between the expected risk of the sample iteration and the minimum expected risk, that is, the expected risk of the regression function:
Techniques like early stopping, L1 and L2 regularization, and dropout are designed to prevent overfitting and underfitting, thereby enhancing the model's ability to adapt to and perform well with new data, thus improving model generalization. [4]
Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages large parameter values. It can also be used to prevent underfitting by controlling the complexity of the model. [15] Ensemble Methods: Ensemble methods combine multiple models to create a more accurate ...
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WASHINGTON (Reuters) -The United States said on Tuesday it was watching events in ally South Korea with "grave concern" after South Korean President Yoon Suk Yeol declared martial law, and said it ...