Python for Deep Learning - Build Neural Networks in Python - Gradient Descent versus Stochastic Gradient Descent

The video tutorial explains the concept of gradient descent, where the entire data set is used in each iteration to measure the gradient and update the cost function's parameters. It contrasts this with stochastic gradient descent, which uses a single value or a subset of values per iteration, making it faster for large data sets. The tutorial highlights the efficiency of stochastic gradient descent in handling large data sets compared to traditional gradient descent.

What is the main difference between gradient descent and stochastic gradient descent?

How does gradient descent measure the gradient during its iterations?

In stochastic gradient descent, what type of data is used to measure the gradient?

What is the impact of data size on the performance of gradient descent?

Why might one choose stochastic gradient descent over gradient descent?