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.

What is the significance of gradient descent in the training process of convolutional neural networks?

Describe the structure of the example image used for training the convolutional neural network.

Explain the role of the convolutional filter in the training process.

What assumptions are made regarding the padding and stride during the convolution operation?

How does the max pooling operation affect the size of the feature map?

What is the purpose of flattening the feature map before feeding it into the logistic unit?

Describe the loss function used in the example and its significance in training.