Deep Learning - Deep Neural Network for Beginners Using Python - How Gradient Descent Works

The video tutorial explains the concept of gradient descent, focusing on moving from a starting point to a target point on the X-axis by minimizing error. It highlights the role of derivatives in determining direction and emphasizes the importance of taking steps in the opposite direction of the derivative to reach the goal. The tutorial also introduces the concept of learning rate to adjust step size and update weights effectively, ensuring convergence to the desired point.

What is the significance of the derivative in the context of gradient descent?

Explain how the concept of slope is related to the direction of movement in gradient descent.

What role does the learning rate play in the gradient descent algorithm?

Describe the process of updating weights in gradient descent.

How does the algorithm ensure that it moves towards the goal point in gradient descent?