Deep Learning CNN Convolutional Neural Networks with Python - Why Derivatives

The video tutorial explains a simple convolutional neural network (CNN) with a focus on its components, including the use of a sigmoid function for classification. It delves into the calculation of Y hat, a key output of the network, and discusses the role of derivatives in optimization. The tutorial introduces the concept of gradient descent, an iterative algorithm used to find the minimum of a function, and highlights the importance of derivatives in this process. The video sets the stage for a deeper exploration of the chain rule in the next session.

What is the role of the sigmoid function in the CNN example discussed?

• To classify the output

• To apply ReLU nonlinearity

• To perform max pooling

• To initialize weights

In the CNN example, what is the size of the convolutional mask used?

• 9 by 9

• 7 by 7

• 5 by 5

• 3 by 3

How is the output Y hat mathematically represented in the CNN?

• As a max pool of weighted sum

• As a linear function of weighted sum

• As a sigmoid of weighted sum

• As a ReLU of weighted sum

What is the dimension of the feature vector F in the CNN example?

• 256 by 1

• 512 by 1

• 1024 by 1

• 128 by 1

Which component in the CNN does not have trainable parameters?

• Convolutional layer

• Fully connected layer

• Sigmoid function

• Pooling layer

What does a derivative represent in the context of a function?

• The rate of change

• The minimum value

• The sum of all values

• The maximum value

What are points called where the derivative of a function is zero?

• Inflection points

• Local extrema

• Global extrema

• Critical points

What is the purpose of computing derivatives in gradient descent?

• To find the minimum value

• To find the maximum value

• To find the average value

• To find the median value

In gradient descent, in which direction should you move to find a minimum?

• In the direction of the gradient

• Opposite to the gradient

• Perpendicular to the gradient

• Parallel to the gradient

What is the iterative algorithm called that uses derivatives to find a function's minimum?

• Simulated annealing

• Gradient descent

• Gradient ascent

• Newton's method