want to get started with deep learning? prototyping a deep learning … · 2018-11-17 · want to...
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![Page 1: Want to get started with deep learning? Prototyping a deep learning … · 2018-11-17 · Want to get started with deep learning? Prototyping a deep learning image classifier Thomas](https://reader033.vdocument.in/reader033/viewer/2022052803/5f26b64672422225630abbc3/html5/thumbnails/1.jpg)
Want to get started with deep learning?
Prototyping a deep learning image classifier
Thomas Ellebæk, Ferring Pharmaceuticals,
ML02 PhUSE EU Connect 2018,
November 5th 2018
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Pattern detection
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Classification
{Alligator, Beaver, Cat, Dog, …, Zebra}
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Classification
{Alligator, Beaver, Cat, Dog, …, Zebra}
0.01 0.00 0.87 0.07 … 0.00
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Deep learning applicationsTeradata report based on survey conducted July 2017:
”80% report that some form of AI is already in production in their organization”(EB9867_State_of_Artificial_Intelligence_for_the_Enterprises.pdf)
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Deep learning applicationsTeradata report based on survey conducted July 2017:
”80% report that some form of AI is already in production in their organization”(EB9867_State_of_Artificial_Intelligence_for_the_Enterprises.pdf)
www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research/8
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Deep learning breakthrough
ImageNet results (top-5)
Err
or
rate
AlexNet
16.4
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Feedforward Neural Network
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Feedforward Neural Network
Shoe size
Hair length
Male
Female
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Feedforward Neural Network
Neuron output:
Activation function:(rectified linear unit – ReLU)
Shoe size and
hair lengthGender
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Feedforward Neural Network
Neuron output:
Activation function:(rectified linear unit – ReLU)
Shoe size and
hair lengthGender
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Feedforward Neural Network
Neuron output:
Activation function:(rectified linear unit – ReLU)
Shoe size and
hair lengthGender
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Feedforward Neural Network
Neuron output:
Activation function:(rectified linear unit – ReLU)
Loss function:(categorical cross entropy)
Shoe size and
hair lengthGender
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Convolutional Neural Network
https://se.mathworks.com/discovery/convolutional-neural-network.html
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Convolution
2D convolution
a b c d
e f g h
i j k l
m n o p
Input
Kernel
… …
… … …
… … …
e
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Convolution
2D convolution 2D cross-correlation
a b c d
e f g h
i j k l
m n o p
Input
Kernel
… …
… … …
… … …
e … …
… … …
… … …
e
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Max pooling
1 4 9 16
2 3 8 15
5 6 7 14
10 11 12 13
4 16
11 14
Input
(2,2) max pooling
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CASE: Cell Counter
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Computing environment
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1 2 3 4 >=5
1 0 0 0 79 0
2 0 0 0 96 0
3 0 0 0 56 0
4 0 0 0 157 0
>=5 0 0 0 52 0
Predicted labels
Act
ual
lab
els
Fitting AlexNet-type model
Validation accuracy: 35.7%
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Final model
3 convolutional layers and
2 dense fully connected
layers
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Final model
3 convolutional layers and
2 dense fully connected
layers
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Error analysis
2 correct predicted examples
5 wrongly predicted examples
Test accuracy: 55.9% >> 35.7%
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Live demo!
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Takeaways and suggested learning
https://machinelearningmastery.com/
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Takeaways and suggested learning
‣ Technology is ready
‣ Data is the most important asset
https://machinelearningmastery.com/
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Takeaways and suggested learning
‣ Technology is ready
‣ Data is the most important asset
https://machinelearningmastery.com/
Questions?