Transcription of Convolutional Neural Networks (CNNs / ConvNets)
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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.
If you’re a fan of the brain/neuron analogies, ever y entr y in the 3D output volume can also be interpreted as an output of a neuron that looks at only a sma ll region in the input and shares parameters with all neurons to the left and right spatially (since these numbers all result from applying the same lter). ...
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