Deep Learning - Deep Neural Network for Beginners Using Python - Logistic Regression Algorithm

The transcript discusses an algorithm that starts with random weights and a bias, updating them for each data point until the error is minimized. It draws a comparison with the Perceptron algorithm, hinting at a small but significant difference, which will be discussed after implementing logistic regression.

What are the main components involved in the algorithm discussed in the lecture?

How do the weights (W) and bias (B) get updated in the algorithm?

What is the stopping criterion for the algorithm mentioned in the lecture?

In what way is the discussed algorithm similar to the Perceptron algorithm?

What hint was provided to understand the difference between the Perceptron algorithm and logistic regression?