- Convolution is a simple mathematical operation, so the enormous complexity involved in implementing convolutional layers may be surprising.
- Instead of dealing with networks, I take the point of view that a convolutional layer is simply a differentiable function.
- Full-blown ConvNets may incorporate a variety of ideas and mechanisms, but in the following I’m going to focus on their very core: convolutional layers.
- Convolutional Neural Networks (CNNs or ConvNets in short) give the state-of-the-art results in many problem domains.
- The post is about the nuts and bolts: algorithms, implementations and optimizations.
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@kdnuggets: “Laws, Sausages and #ConvNets – great overview #DeepLearning #NeuralNets”
Laws, like sausages, cease to inspire respect in proportion as we know howthey are made.
Laws, Sausages and ConvNets