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Gradient Descent: Conjugate gradient method, Stochastic gradient descent, Newtons method in optimization, Optimization (mathematics), Delta rule, Wolfe conditions, Algorithm -

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2026-03-21
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.Gradient descent is a first-order optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. If instead one takes steps prop ... Pilns apraksts

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online.Gradient descent is a first-order optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. If instead one takes steps proportional to the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent. Gradient descent is also known as steepest descent, or the method of steepest descent. When known as the latter, gradient descent should not be confused with the method of steepest descent for approximating integrals.

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Izdevējs OmniScriptum
Izlaides gads 2026
Vāka tips Mīkstais vāks
EAN 9786130205485
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109,28 € 182,14 €