Transcription of Introduction to Convolutional Neural Networks
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Introduction to Convolutional Neural Networks
Introduction to Convolutional Neural NetworksJianxin WuLAMDA GroupNational Key Lab for Novel Software TechnologyNanjing University, 1, 2017 Contents1 Introduction22 Tensor and vectorization .. Vector calculus and the chain rule ..43 CNN in a The architecture .. The forward run .. Stochastic gradient descent (SGD) .. Error back propagation ..84 Layer input, output and notations95 The ReLU layer106 The convolution What is convolution? .. Why to convolve? .. Convolution as matrix product .. The Kronecker product .. Backward propagation: update the parameters .. Even higher dimensional indicator matrices .. Backward propagation: prepare supervision signal for the previ-ous layer .. Fully connected layer as a convolution layer .. 227 The pooling layer2318 A case study: the VGG-16 VGG-Verydeep-16.
the classes based on the identity of its main object, e.g., dog, airplane, bird, etc. 2 Preliminaries We start by a discussion of some background knowledge that are necessary in order to understand how a CNN runs. One can ignore this section if he/she is familiar with these basics. 2.1 Tensor and vectorization
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