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Convolutional neural networks (CNN) Deep Learning - Part 3

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Authors: AvatarKaivan Kamali

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last_modification Last modification: Jun 2, 2022

What is a convolutional neural network (CNN)?

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What is a convolutional neural network (CNN)?


Convolutional Neural Network (CNN)


Feedforward neural networks (FNN)

Neurons forming the input, output, and hidden layers of a multi-layer feedforward neural network.


Limitations of FNN


Limitations of FNN


Limitations of FNN


Inspiration for CNN


Inspiration for CNN


Architecture of CNN


Input layer


Convolution layer


3 by 3 Filter

A 3 by 3 filter applied to a 4 by 4 image, resulting in a 2 by 2 image.


Convolution operator parameters


Filter size


Padding


3 by 3 filter with padding of 1

A 3 by 3 filter applied to a 5 by 5 image, with padding of 1, resulting in a 5 by 5 image.


Stride


3 by 3 filter with stride of 2

A 3 by 3 filter applied to a 5 by 5 image, with stride of 2, resulting in a 2 by 2 image.


Dilation


3 by 3 filter with dilation of 2

A 3 by 3 filter applied to a 7 by 7 image, with dilation of 2, resulting in a 3 by 3 image.


Activation function


Relu activation function

Two matrices representing filter output before and after ReLU activation function is applied.


Single channel 2D convolution

One matrix representing an input vector and another matrix representing a filter, along with calculation for single input channel two dimensional convolution operation.


Triple channel 2D convolution

Three matrices representing an input vector and another three matrices representing a filter, along with calculation for multiple input channel two dimensional convolution operation.


Triple channel 2D convolution in 3D

Multiple cubes representing input vector, filter, and output in a 3 channel 2 dimensional convolution operation.


Change channel size


Pooling layer


Fully connected layer


An example CNN

A convolutional neural network with 3 convolution layers followed by 3 pooling layers.


An example CNN


MNIST dataset

Classification of MNIST images with CNN


For references, please see tutorial’s References section


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