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What is Stochastic Gradient Descent?
In Gradient Descent, we take all of the rows of the dataset, and feed them into the neural network and once it is done, we calculate the cost function and adjust the weights. This is called Gradient Descent method, also know as, Batch Gradient Descent method because we are taking whole batch of rows into consideration while training the neural network.
In Stochastic Gradient Descent, instead of taking the whole rows into consideration, we take a single instance or a single row, and feed that single instance to the neural network. After doing that we calculate the cost function and then adjust the weights. Then it selects the second instance randomly and repeats the process again.
Stochastic Gradient Descent has capability to find the global maximum, and it takes relatively less time than Batch Gradient Descent algorithm. The reason is that it does not require to take all the instances into the memory and wait till the end for the result.
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