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Where's Waldo? The Power of CNNs
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This excerpt from Dive into Deep Learning explores the evolution of convolutional neural networks (CNNs) from basic multi-layered perceptrons (MLPs). It begins by showing the limitations of MLPs in processing high-dimensional data like images, particularly the large number of parameters required. The excerpt then introduces the concepts of translation invariance and locality, which are crucial for building effective CNNs. These concepts are then applied mathematically to derive the structure of a convolutional layer, where a convolutional kernel is used to weigh pixel intensities in a local region. Finally, the excerpt discusses the importance of channels in images and how they are integrated into convolutional operations, leading to the formation of feature maps. By incorporating these principles, CNNs effectively reduce the number of parameters needed, making image processing more efficient and allowing for the learning of complex features from data.
Read more here: https://d2l.ai/chapter_convolutional-neural-networks/why-conv.html
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