Stochastic Gradient Ascent


Stochastic Gradient Ascent is an optimization algorithm used in machine learning to find the maximum likelihood or posterior probability of a model by iteratively updating the parameters of the model in the direction of the gradient of the log-likelihood or log-posterior probability. Unlike batch gradient ascent, which updates the parameters using the entire dataset, stochastic gradient ascent updates the parameters using a randomly selected subset of the data, making it more computationally efficient and scalable to large datasets. Stochastic gradient ascent is commonly used in training models such as logistic regression, neural networks, and support vector machines.


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