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CNN Explained Visually: Padding, Stride, Pooling, Receptive Fields, Dilation & Layer Architecture

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CNN Explained Visually: Padding, Stride, Pooling, Receptive Fields, Dilation & Layer Architecture

In this video, we understood the core building blocks of a convolutional neural network. We started with convolution and saw how filters extract features like edges, textures, and shapes from images. Then we looked at important concepts like padding and stride, and how they affect the output dimensions and the flow of information through the ...

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在视频中查找 17:31Backpropagation on max pooling works by selecting the maximum item in each window

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