Gradient descent: Difference between revisions

Batch gradient descent updates the parameters after computing the gradient of a cost function over all training datasets. Although it can be computationally expensive for large datasets, it can converge to the global minimum in terms of this cost function.

Stochastic gradient descent updates the parameters after computing the gradient of a cost function for one training example. It is less computationally expensive than batch gradient descent, though it may converge to a local minimum in the cost function.

Mini-batch gradient descent is a method for updating parameters after computing the gradient of a cost function for a small set of training examples. It offers an alternative to batch gradient descent and stochastic gradient descent, being less computationally intensive than batch gradient descent and capable of convergeng to either global minimums or local minima in the cost function.

Gradient descent can also be enhanced with regularization techniques, which reduce overfitting and enhance generalization of the model. Regularization techniques like L1 or L2 regularization add a penalty term to the cost function that penalizes large parameter values; this encourages models to use smaller parameter values while helping prevent overfitting.

To expedite your descent down the mountain, take steps in the direction that will bring you down fastest. You can tell which way to go by looking at the slope beneath your feet; if one direction is steeper than another, that is likely where you should head.

Machine learning utilizes gradient descent to determine the best values for certain parameters that influence predictions. We examine how changing these parameters affects how accurately our predictions match actual outcomes, and use gradient descent to find these values that will give our forecasts maximum precision.