Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Network Architecture Activity

The video tutorial explains the process of building a convolutional neural network model. It starts with defining the initial image dimensions and setting up the model. Each convolutional layer is followed by a max pooling layer, which reduces the spatial dimensions by half while doubling the number of channels. This process continues until the spatial dimensions are less than ten. At this point, a fully connected layer with 1000 units is added, followed by a softmax layer. The tutorial concludes with instructions on calculating the total number of parameters in the model.

How does the number of channels change after each convolutional layer?

What happens to the spatial dimensions after each pooling layer in a convolutional neural network?

What is the stopping criterion for adding layers in the described model?

Explain the role of the fully connected layer in the model described.

What is the purpose of the softmax layer in the model?