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in the case of large-scale machine learning problems.

Iterative method

thumbIn stochastic (or "on-line") gradient descent, the true gradient of is approximated by a gradient at a single sample:

As the algorithm sweeps through the training set, it performs the above update for each training sample. Several passes can be made over the training set until the algorithm converges. If this is done, the data can be shuffled for each pass to prevent cycles. Typical implementations

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