Convolution in Practice
Explore how convolutional layers use multi-dimensional kernels to generate feature maps from 3D images, adjusting spatial dimensions with stride and padding. Learn why pooling layers downsample features to improve invariance and abstraction in CNNs, enhancing your understanding of neural network architecture in practical applications.
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When it comes to real-life applications, most images are in fact a 3D tensor with width, height, and 3 channels (R,G,B) as dimensions.
In that case, the kernel should also be a 3D tensor ( ...