Stochastic Gradient Descent: Revision history

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15 April 2024

  • curprev 18:2518:25, 15 April 2024‎ Rice talk contribs‎ 753 bytes +753‎ Created page with " = How it works = First, a weight <math>\bf{w}</math> is selected. This is the starting point from which we iteratively improve the solution. For ''each datapoint'' in the dataset, the ''gradient'' of the loss function with respect to weights is computed and a learning rate is selected. These two statistics determine the speed and direction the model <math>\bf{w}</math> converges to. Then, a '''GD update rule''' is used to converge the weights to the desired outcome ba..." Tag: Visual edit
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