Gradient descent: Difference between revisions

Gradient descent (view source)

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 {{see also|Machine learning terms}}
 ==Introduction==
-Gradient descent is a popular optimization algorithm in machine learning. It works by finding the minimum cost function, which can be adjusted to minimize errors between predicted output and actual output from a model. Gradient descent utilizes weights and biases as input parameters to achieve this minimal error margin.
+Gradient descent is a popular optimization algorithm in machine learning. Its goal is to minimize the [[loss]] of the model during [[training]].  Gradient descent utilizes weights and biases as input parameters to achieve this minimal error margin.
 ==How Gradient Descent Works==