Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: 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 demonstrate the training process, including convolution, ReLU activation, max pooling, and flattening. The tutorial also covers the logistic unit with sigmoid nonlinearity and the squared loss function, emphasizing the parameters involved in minimizing the loss. The example serves as a foundation for understanding more complex CNNs.

How does max pooling affect the dimensions of the feature map?

What is the process of flattening the feature map, and why is it necessary?

Define the loss function used in the example and explain its components.