Convolutional Neural Networks (CNNs / ConvNets)

Table of Contents:Architecture OverviewConvNet LayersConvolutional LayerPooling LayerNormalization LayerFully-Connected LayerConverting Fully-Connected Layers to Convolutional LayersConvNet ArchitecturesLayer PatternsLayer Sizing PatternsCase Studies (LeNet / AlexNet / ZFNet / GoogLeNet / VGGNet)Computational ConsiderationsAdditional ReferencesConvolutional Neural Networks (CNNs / ConvNets) Convolutional Neural Networks are very similar to ordinary Neural Networks from the previouschapter: they are made up of neurons that have learnable weights and biases. Each neuronreceives some inputs, performs a dot product and optionally follows it with a non-linearity. Thewhole network still expresses a single differentiable score function: from the raw image pixels onone end to class scores at the other.

the nal c lass scores. Note that som e layers contain parameters and othe r don’t. In par ticular, the CONV/FC layers per form transforma tions that are a function of not on ly the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other

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