Gradient Descent, Data Science, Data Visualization, Deep Learning, Machine Learning.
This is because in SGD, we need to compute the gradient for each individual data point, while in BGD, we compute the gradient over the entire dataset. For example, if we have 10,000 data points and we train for 10 epochs, then with SGD, we will perform 100,000 (10,000 x 10) calculations to update