Translations:Stochastic Gradient Descent/1/en: Difference between revisions

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Message definition (Stochastic Gradient Descent)

'''Stochastic {{Term|gradient descent}}''' (often abbreviated '''{{Term|SGD|SGD}}''') is an iterative optimisation algorithm used to minimise an {{Term|objective function|objective function}} written as a sum of differentiable sub-functions. It is the workhorse behind modern machine-learning training, powering everything from {{Term|logistic regression}} to deep neural networks.

Stochastic gradient descent (often abbreviated SGD) is an iterative optimisation algorithm used to minimise an objective function written as a sum of differentiable sub-functions. It is the workhorse behind modern machine-learning training, powering everything from logistic regression to deep neural networks.

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	'''Stochastic gradient descent''' (often abbreviated '''{{Term\|SGD\|SGD}}''') is an iterative optimisation algorithm used to minimise an {{Term\|objective function\|objective function}} written as a sum of differentiable sub-functions. It is the workhorse behind modern machine-learning training, powering everything from logistic regression to deep neural networks.		'''Stochastic {{Term\|gradient descent}}''' (often abbreviated '''{{Term\|SGD\|SGD}}''') is an iterative optimisation algorithm used to minimise an {{Term\|objective function\|objective function}} written as a sum of differentiable sub-functions. It is the workhorse behind modern machine-learning training, powering everything from {{Term\|logistic regression}} to deep neural networks.