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?

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

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

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

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

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