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 was the error function previously referred to as?

• Sigmoid function

• Cross entropy

• Output formula

• Gradient descent

Which of the following is NOT an input required for the gradient descent method?

• Learning rate

• Labels (Y)

• Activation function

• Features (X)

How is the derivative of the error calculated?

• Bias minus weights

• Y minus output

• Output minus Y

• Weights minus bias

What is the purpose of the learning rate in the gradient descent method?

• To initialize the weights

• To calculate the error

• To adjust the step size for updating weights

• To determine the number of iterations

What will be implemented in the next lecture?

• Sigmoid function

• Cross entropy

• Error function

• Training function of logistic regression