Deep Learning - Deep Neural Network for Beginners Using Python - Introduction to Gradient Descent

The video tutorial introduces the cross entropy error function, highlighting its implementation in Python and its effectiveness in minimizing error and maximizing probability. It explains the concept of P of Yi, relating it to the sigmoid function. The tutorial then introduces gradient descent, using a mountain analogy to illustrate error reduction. It discusses challenges like local minima and maxima, and concludes with a simple example of global minima.

What is the primary purpose of the cross entropy error function?

• To calculate the sum of squared errors

• To measure the difference between predicted and actual probabilities

• To increase the error in predictions

• To maximize the output function

How is the probability of Yi determined in the context of minimizing error?

• By calculating the mean squared error

• By applying the sigmoid function to WX + B

• By using a random number generator

• By using a linear function

What is the main goal of gradient descent?

• To minimize the error function

• To find the maximum error

• To calculate the average error

• To increase the error function

In the mountain analogy, what does reaching the ground level signify?

• The probability is zero

• The error is zero or minimal

• The error is at its maximum

• The function is undefined

What challenge does the mountain analogy highlight in gradient descent?

• The issue of local minima and maxima

• The difficulty in calculating the error

• The inability to find any minima

• The presence of multiple global minima

What does the ball analogy simplify in the context of gradient descent?

• The increase in error function

• The calculation of error

• The idea of a single global minima

• The concept of multiple peaks

What is the significance of a global minima in gradient descent?

• It represents the highest error

• It indicates the start of the descent

• It is irrelevant to the process

• It is the point where error is minimized