Deep Learning CNN Convolutional Neural Networks with Python - Example Setup

The video tutorial explains the gradient descent process in convolutional neural networks (CNNs), highlighting its complexity compared to plain neural networks. It uses a simple example of a 32x32 grayscale image to illustrate the process, including convolution with a 5x5 filter, ReLU activation, max pooling, and flattening. The tutorial also covers the logistic unit with sigmoid nonlinearity and the squared loss function. The focus is on understanding the parameters involved and setting up a stochastic gradient descent algorithm for CNNs.

Why is gradient descent more complex in convolutional neural networks compared to plain neural networks?

• Because CNNs use different activation functions.

• Due to the presence of convolutional layers.

• Because CNNs have more layers.

• Due to the larger size of input data.

What is the size of the convolutional filter used in the example?

• 3 by 3

• 7 by 7

• 9 by 9

• 5 by 5

What is the purpose of applying ReLU nonlinearity in the convolutional process?

• To reduce the size of the feature map.

• To introduce non-linearity into the model.

• To perform dimensionality reduction.

• To increase the number of channels.

What is the result of applying max pooling with a 2 by 2 size and stride of 2?

• The feature map size is reduced by a quarter.

• The feature map size is doubled.

• The feature map size is reduced by half.

• The feature map size remains the same.

What is the length of the vector obtained after flattening the feature map?

• 128 by 1

• 1024 by 1

• 256 by 1

• 512 by 1

What type of nonlinearity does the logistic unit use?

• Tanh

• Softmax

• Sigmoid

• ReLU

What is the form of the loss function used in the example?

• Hinge loss

• Mean squared error

• Cross-entropy loss

• Huber loss