Deep Learning - Deep Neural Network for Beginners Using Python - Solution and Regularization

The video tutorial discusses the classification of points using two equations with different weights. It explains how the sigmoid function is used to classify points as green or red based on their values. The tutorial compares two equations, highlighting that larger weights can improve precision but may lead to overfitting. Regularization is introduced as a solution to prevent overfitting by controlling the coefficients.

What is the threshold used to classify a point as green in the sigmoid function?

• 0.5

• 0.7

• 0.2

• 0.9

What is the sigmoid of 2 according to the transcript?

• 0.99

• 0.5

• 0.88

• 0.12

How does the second equation with weights of 10 compare to the first equation in terms of classification accuracy?

• It is less accurate

• It is equally accurate

• It is more accurate

• It does not classify at all

What is the sigmoid value of -20 as mentioned in the transcript?

• 0.88

• 0.5

• 0.000021

• 0.12

What is a potential downside of using larger weights in neural networks?

• Increased computational cost

• Slower convergence

• Underfitting

• Overfitting

What is the purpose of regularization in the context of neural networks?

• To reduce computational cost

• To speed up training

• To prevent overfitting

• To increase training accuracy

Why might a model with large coefficients not perform well on validation data?

• Because of underfitting

• Due to overfitting

• Because of slow convergence

• Due to high bias