Deep Learning CNN Convolutional Neural Networks with Python - 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 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.

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