Python for Deep Learning - Build Neural Networks in Python - What is Gradient Descent?

The video tutorial explains how networks can learn by themselves using the gradient descent algorithm. It covers the process of adjusting weights to minimize a cost function, using calculus to iteratively refine parameters. The tutorial illustrates the concept with a graph, showing how different iterations affect the cost function. It also explains the formula used in gradient descent and how the step size is determined by the gradient's slope, ultimately leading to the minimum value.

Explain how gradient descent adjusts the weights in a network.

What is gradient descent and why is it important in machine learning?

How does the cost function change with each iteration in gradient descent?

Describe the relationship between the gradient and the step size in gradient descent.

What does it mean when the gradient is high in the context of gradient descent?