Deep Learning CNN Convolutional Neural Networks with Python - Deep Neural Network Architecture Activity

The video tutorial explains how to build a convolutional neural network model using RGB images with dimensions 224x224x3. It describes the process of adding convolutional layers followed by max pooling layers, which reduce the spatial dimensions by half and double the number of channels. This process continues until the spatial dimensions are smaller than 10. A fully connected layer with 1000 units and a softmax layer is added at the end. The tutorial concludes with a task to calculate the total number of parameters in the model.

What should be the stopping condition for adding layers in the model?

What is the role of the fully connected layer in the model described?