deep learning and tensorflow...deep learning: a theoretical introduction –episode 3 [1]deep...
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[1]Deep Learning: a theoretical introduction – Episode 3
Deep Learningand TensorFlowEpisode 3 Deep Convolutional Neural Networks
Università degli Studi di Pavia
[2]Deep Learning: a theoretical introduction – Episode 3
The storm ofDeep Convolutional Neural Networks
(DCNN)
[3]Deep Learning: a theoretical introduction – Episode 3
ImageNet Challenge▪
[4]Deep Learning: a theoretical introduction – Episode 3
ImageNet Challenge▪
[5]Deep Learning: a theoretical introduction – Episode 3
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The Mother of all DCNNs
[6]Deep Learning: a theoretical introduction – Episode 3
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The Mother of all DCNNs
[7]Deep Learning: a theoretical introduction – Episode 3
DCNN Building Blocks(layerwise)
[8]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer▪
[9]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer▪
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[10]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer▪
[11]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer▪
[12]Deep Learning: a theoretical introduction – Episode 3
Max Pooling Layer▪
[13]Deep Learning: a theoretical introduction – Episode 3
Local Response Normalization Layer▪
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[14]Deep Learning: a theoretical introduction – Episode 3
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AlexNet Architecture
[15]Deep Learning: a theoretical introduction – Episode 3
AlexNet Gradient▪
Loss Function
[16]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
m
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[17]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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*W X
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[18]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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*W X
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[19]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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*W X
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[20]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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*W X
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Yij
[21]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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*W X
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X
[22]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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[23]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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[24]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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ReLU
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[25]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
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ReLU
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Yij = 1 = 0
[26]Deep Learning: a theoretical introduction – Episode 3
Max Pooling Gradient▪
[27]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
[28]Deep Learning: a theoretical introduction – Episode 3
Convolutional Layer Gradient▪
[29]Deep Learning: a theoretical introduction – Episode 3
LRN Gradient▪
[30]Deep Learning: a theoretical introduction – Episode 3
LRN Gradient▪
[31]Deep Learning: a theoretical introduction – Episode 3
LRN Gradient▪
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LRN
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Yijl
[32]Deep Learning: a theoretical introduction – Episode 3
LRN Gradient▪
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LRN
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[33]Deep Learning: a theoretical introduction – Episode 3
ImageNet Challenge▪
[34]Deep Learning: a theoretical introduction – Episode 3
AlexNet (Krizhevsky, Sutskever & Hinton, 2012)
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[35]Deep Learning: a theoretical introduction – Episode 3
Deep Convolutional Neural Networks (DCNN)▪
[36]Deep Learning: a theoretical introduction – Episode 3
Inside AlexNet(after training)
[37]Deep Learning: a theoretical introduction – Episode 3
AlexNet Filters (after training)
[38]Deep Learning: a theoretical introduction – Episode 3
AlexNet Filters- DeconvNet
[39]Deep Learning: a theoretical introduction – Episode 3
AlexNet Filters- DeconvNet
[40]Deep Learning: a theoretical introduction – Episode 3
AlexNet Filters- DeconvNet
[41]Deep Learning: a theoretical introduction – Episode 3
AlexNet Filters- DeconvNet
[42]Deep Learning: a theoretical introduction – Episode 3
Beyond AlexNet:The DCNN storm
[43]Deep Learning: a theoretical introduction – Episode 3
ImageNet: the full story
[44]Deep Learning: a theoretical introduction – Episode 3
VGG Architecture
[45]Deep Learning: a theoretical introduction – Episode 3
Inception Acrhitecture▪
[46]Deep Learning: a theoretical introduction – Episode 3
Inception Architecture▪
[47]Deep Learning: a theoretical introduction – Episode 3
Inception Architecture▪
h
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Inception Architecture▪
[49]Deep Learning: a theoretical introduction – Episode 3
Inception Architecture▪
256 480 480512
512 512 832
832 1024
[50]Deep Learning: a theoretical introduction – Episode 3
Inception Architecture▪
[51]Deep Learning: a theoretical introduction – Episode 3
Inception Architecture▪
[52]Deep Learning: a theoretical introduction – Episode 3
ResNet Architecture▪
[53]Deep Learning: a theoretical introduction – Episode 3
ResNet Architecture▪
[54]Deep Learning: a theoretical introduction – Episode 3
Comparing Different DCNNs▪
[55]Deep Learning: a theoretical introduction – Episode 3
Comparing Different DCNNs
[56]Deep Learning: a theoretical introduction – Episode 3
Do DCNNs Dreamof Electric Sheep?
[57]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
[58]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
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Feature Enhancement▪
k l I
[60]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
[61]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
[62]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
[63]Deep Learning: a theoretical introduction – Episode 3
Can DCNNs 'dream'?
[64]Deep Learning: a theoretical introduction – Episode 3
The Power of Abstraction(in layers)
[65]Deep Learning: a theoretical introduction – Episode 3
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The Power of Abstraction
[66]Deep Learning: a theoretical introduction – Episode 3
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The Power of Abstraction
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Mixing Two Images▪
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The Power of Abstraction
[69]Deep Learning: a theoretical introduction – Episode 3
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The Power of Abstraction
[70]Deep Learning: a theoretical introduction – Episode 3
Human-like Vision?
[71]Deep Learning: a theoretical introduction – Episode 3
A DCNN can be fooled…
[72]Deep Learning: a theoretical introduction – Episode 3
Reconstructing Images from Feature Maps
[73]Deep Learning: a theoretical introduction – Episode 3
Reconstructing Images from Feature Maps▪
k l I
[74]Deep Learning: a theoretical introduction – Episode 3
Reconstructing Images from Feature Maps
[75]Deep Learning: a theoretical introduction – Episode 3
Just add some little noise ...
[76]Deep Learning: a theoretical introduction – Episode 3
No Free Lunch:having an annotated dataset
[77]Deep Learning: a theoretical introduction – Episode 3
Generative Adversarial Network▪
[78]Deep Learning: a theoretical introduction – Episode 3
Active Learning
[79]Deep Learning: a theoretical introduction – Episode 3
Transfer Learning
[80]Deep Learning: a theoretical introduction – Episode 3
Transfer Learning
[81]Deep Learning: a theoretical introduction – Episode 3
Image ClassificationObject Detection
Segmentation
[82]Deep Learning: a theoretical introduction – Episode 3
Deep Learning for different imaging tasks
[83]Deep Learning: a theoretical introduction – Episode 3
Semantic segmentation
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[84]Deep Learning: a theoretical introduction – Episode 3
Object detection and positioning
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[85]Deep Learning: a theoretical introduction – Episode 3
Object detection and positioning
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[86]Deep Learning: a theoretical introduction – Episode 3
Object detection and positioning▪