Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Gradients of MaxPooling Layer

This video tutorial extends the basic concepts of convolutional neural networks (CNNs) by exploring the addition of more convolutional filters, neurons in fully connected layers, and convolutional and max pooling layers. It explains how these elements can be applied to multidimensional images with multiple channels, simplifying complex problems into manageable tasks. The tutorial also previews future topics, such as handling classification problems with more than two classes and adding more convolutional layers.

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