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The goal of this package is to illustrate the efficiency of stochastic gradient descent for large-scale learning tasks. Two algorithms, Stochastic Gradient Descent (SGD), and Averaged Stochastic Gradient Descent (ASGD), are applied to two well known problems, Linear Support Vector Machines, and Conditional Random Fields.

The goal of this package is to illustrate the efficiency of stochastic gradient descent for large-scale learning tasks. Two algorithms, Stochastic Gradient Descent (SGD), and Averaged Stochastic Gradient Descent (ASGD), are applied to two well known problems, Linear Support Vector Machines, and Conditional Random Fields.

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2024-Mar-28 to 2024-Apr-27