Deep Learning - Deep Neural Network for Beginners Using Python - Updating Weights in Python

The lecture recaps previous topics and focuses on updating weights using the gradient descent method. It explains how to calculate the output and the derivative of error, and how to update weights and bias in logistic regression. The session concludes with a preview of the next lecture, which will cover the training function of logistic regression.

What is the purpose of the sigmoid function in logistic regression?

Explain the process of updating weights using the gradient descent method.

What are the inputs required for the weight update process in logistic regression?

How is the derivative of error calculated in the context of logistic regression?

What will be implemented in the next lecture regarding logistic regression?