Description: Stochastic gradient is an optimization method used in the field of machine learning. This approach is based on the idea that instead of calculating the gradient of the loss function using the entire dataset, a randomly selected subset of data is used for this calculation. This allows the optimization process to be more efficient and faster, as it reduces the amount of data that needs to be processed in each iteration. The use of a random subset also introduces variability into the optimization process, which can help avoid getting stuck in local minima and improve the model’s ability to generalize to new data. Stochastic gradient is commonly used in various machine learning algorithms, particularly in settings with large datasets where computational costs are a concern. In summary, stochastic gradient is a fundamental technique that enables more efficient model optimization, facilitating learning in complex and dynamic situations.
