Gradient Descent, Data Science, Data Visualization, Deep Learning, Machine Learning.
model parameters are updated more frequently, and the gradient calculation involves a random data point, which introduces more randomness into the optimization process, and the optimizer can escape from a suboptimal local minimum. However, since SGD updates the parameters using only a single data