Deep Learning - Deep Neural Network for Beginners Using Python - Helper Functions (NN Implementation)

The video tutorial covers the sigmoid function and its derivative, often referred to as sigmoid prime. It explains how to calculate the error term in neural networks, using the difference between predicted and actual values, and how to implement this in a neural network model. The tutorial emphasizes understanding the mathematical foundation and practical application of these concepts in machine learning.

What is another term used for the derivative of the sigmoid function?

• Sigmoid double

• Sigmoid prime

• Sigmoid inverse

• Sigmoid square

How is the error term calculated in a neural network?

• By multiplying the predicted and actual values

• By subtracting the predicted value from the actual value

• By adding the predicted and actual values

• By dividing the predicted value by the actual value

In the context of neural networks, what does 'Y hat' represent?

• The error term

• The predicted output

• The actual output

• The input value

What is the purpose of the helper function discussed in the video?

• To update the weights

• To calculate the sigmoid function

• To compute the error term

• To initialize the neural network

What is the next step after writing the helper function in the video?

• Testing the neural network

• Implementing the neural network

• Training the neural network

• Evaluating the neural network