AZ
Uploaded byAbdulrazak Zakieh
PPTX, PDF672 views

Convolutional Neural Network (CNN)

The document discusses convolutional neural networks (CNNs), covering their applications, architecture, and various components such as padding, stride convolution, and layer types. It highlights key principles and mathematical concepts involved in CNNs, including discussions on filters and their functions in detecting features. Additionally, specific examples are provided to illustrate the application of CNNs in real-world scenarios.

Embed presentation

Download to read offline
Convolutional Neural Networks (CNN) BY: ABDULRAZAK ZAKIEH ARTIFICIAL INTELLIGENCE CLASS, 2020-2021 LECTURER: PROF. ÖZLEM AKTAŞ
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 2
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 3
CNNs Applicatios ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 4 level 2 autonomous driving assistance TOGG COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings Jordi Laguarta, Ferran Hueto, Brian Subirana 30 SEP 2020
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 5
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 6 Y output
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 7 Fully Connected Fully Connected
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 8
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 9 ???????
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 10 ???????
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 11 ??????? Does not capture the “natural” invariances we expect in images (translation, scale)
Neural Networks (revision) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 12 256 * 256 RGB image – 10 outputs – 1,966,080 Weights Over fitting the data
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 13
Filters – Horizontal Edges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 15 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 5 × 5 3 × 3 1 1 1 0 0 0 -1 -1 -1
Filters – Horizontal Edges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 16 1 1 1 0 0 0 -1 -1 -1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 1 1 1 000 -1-1-1 39 1 1 1 000 -1-1-1 39 -70 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 39 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 135 * = 5 × 5 3 × 3 3 × 3
Filters – Vertical Edges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 17 -1 0 1 -1 0 1 -1 0 1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 1 1 1 000 -1-1-1 160 1 1 1 000 -1-1-1 160 0 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 160 0 -59160 0 -59 65 160 0 -59 65 67 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 -134 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 -134 55 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 134 55 -39 * = 5 × 5 3 × 3 3 × 3
Filters ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 18
Convolutional Neural Networks (CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 19 Which filter to use?
Convolutional Neural Networks (CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 20 y = z * w
Convolutional Neural Networks (CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 21 “learn” the right filter(s) for the specified task
Content CNN Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 22
Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 23 -1 0 1 -1 0 1 -1 0 1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 160 0 -59 65 67 -31 134 55 -39 * = 5 × 5 3 × 33 × 3
Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 24 “zero pad” the input image
Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 25 -1 0 1 -1 0 1 -1 0 1 0 0 0 0 0 0 0 0 10 80 90 10 24 0 0 41 12 53 13 53 0 0 11 51 79 120 86 0 0 68 13 53 10 15 0 0 45 23 126 12 12 0 0 0 0 0 0 0 0 92 92 -79 -66 -23 143 160 0 -59 -143 76 65 67 -31 -143 87 134 55 -39 -142 36 66 -14 -152 -22 * = 5 × 5 5 × 5 3 × 3 7 × 7 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 26
Stride Convolution ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 27 1 1 1 0 0 0 -1 -1 -1 10 80 90 10 24 23 73 41 12 53 13 53 12 82 11 51 79 120 86 57 23 68 13 53 10 15 12 64 45 23 126 12 12 86 12 72 82 45 65 95 81 51 46 97 71 123 69 8 62 1 1 1 000 -1-1-1 39 1 1 1 000 -1-1-1 39 -70 1 1 1 000 -1-1-11 1 1 000 -1-1-1 1 1 1 000 -1-1-1 39 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 891 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 135 * = 7 × 7 3 × 3 3 × 3 Stride = 2
Width and Height of the Output ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 28 Filter Size: f × f Padding: p Stride: s Input width: 𝑛 𝑤 Input height: 𝑛ℎ [ 𝑛 𝑤 + 2𝑝 − 𝑓 𝑠 + 1 , 𝑛ℎ + 2𝑝 − 𝑓 𝑠 + 1]
Valid and Same Convolution ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 29 Same Valid 50 * 50 n * n SameCONV s =1 p=(f –1) / 2 50 * 50 50 * 50 CONV 30 * 30
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Example ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 30
Convolution over Volume ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 31 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 5 × 5 × 3 3 × 3 × 3 = 3939 -7039 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 39 -70 -161 -28 2 41 39 -70 -161 -28 2 41 -53 39 -70 -161 -28 2 41 -53 89 39 -70 -161 -28 2 41 -53 89 135 R-G-B 𝒏 𝒘 × 𝒏 𝒉 × 𝒏 𝒄 𝒇 𝒉 × 𝒇 𝒘 × 𝒏 𝒄 𝒏 𝒘 𝟏 × 𝒏 𝒉 𝟏 3 × 3
Convolution over Volume ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 32 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 R-G-B 5 × 5 × 3 3 × 3 × 3 = 39 -70 -161 -28 2 41 -53 89 135 -1 0 1 -1 0 1 -1 0 1 * -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 3 × 3 × 3 12 53 10 81 24 -11 61 -12 -53 39 -70 -161 -28 2 41 -53 89 135 12 53 10 81 24 -11 61 -12 -53 3 × 3 3 × 3 3 × 3 × 2
Convolution over Volume ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 33 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 = -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 39 -70 -161 -28 2 41 -53 89 135 12 53 10 81 24 -11 61 -12 -53 CONV 3 × 3 × 3 #2 5 × 5 × 3 3 × 3 × 2 CONV f = 3 5 × 5 × 3 3 × 3 × 2
Convolution over Volume ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 34 Number of channels for each filter? 3, 4, 5 Number of filter? 3, 4, 5
Convolutional Neural Networks (CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 35 Mathematical: It’s correlation, not convolution
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Example ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 36
Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 37 Max-PoolingCONV Fully Connected
Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 38 Max-PoolingCONV Fully Connected 𝑧𝑖+1 = 𝑧𝑖 ∗ 𝑊𝑖
Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 39 Max-PoolingCONV 𝑧𝑖+1 = 𝑧𝑖 ∗ 𝑊𝑖 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 9090 12090 120 12090 120 120 79 90 120 120 79 120 90 120 120 79 120 120 90 120 120 79 120 120 126 90 120 120 79 120 120 126 126 90 120 120 79 120 120 126 126 126 𝑓 = 3 S = 1
Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 40 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 8080 9080 90 68 80 90 68 86 𝑓 = 2 S = 2
74 67 82 97 74 14 17 12 66 27 42 67 82 34 75 6 17 78 63 97 Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 41 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 80 90 68 86 𝑓 = 2 S = 2 𝑀𝐴𝑋 𝑃𝑂𝑂𝐿
74 67 82 97 74 14 17 12 66 27 42 67 82 34 75 6 17 78 63 97 Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 42 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 80 90 68 86 𝑓 = 2 S = 2 𝑀𝐴𝑋 𝑃𝑂𝑂𝐿 Fully Connected
Content CNNs Applications Neural Networks (revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 43
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 44 LeNet-5 ~ 60𝐾
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 45 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet ~ 60𝑀
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 46 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 47 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 48 Simonyon & Zisserman 2015 (~138 M) VGG - 16
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 49 Deep Residual Learningfor ImageRecognition (He.K et al. 2015) ResNet
CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 50 Going Deeper with Convolutions (Christian Szegedy et. al. 2015) GoogLeNet
Thanks for listening ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 51

More Related Content

PPTX
Machine Learning - Convolutional Neural Network
byRichard Kuo
 
PPTX
Convolutional Neural Networks
byAshray Bhandare
 
PDF
Deep Learning for Personalized Search and Recommender Systems
byBenjamin Le
 
PDF
Faster R-CNN - PR012
byJinwon Lee
 
PDF
Overview of Convolutional Neural Networks
byananth
 
PDF
Convolutional Neural Networks (CNN)
byGaurav Mittal
 
PPTX
Convolution Neural Network (CNN)
byBasit Rafiq
 
PPTX
Counter propagation Network
byAkshay Dhole
 
Machine Learning - Convolutional Neural Network
byRichard Kuo
 
Convolutional Neural Networks
byAshray Bhandare
 
Deep Learning for Personalized Search and Recommender Systems
byBenjamin Le
 
Faster R-CNN - PR012
byJinwon Lee
 
Overview of Convolutional Neural Networks
byananth
 
Convolutional Neural Networks (CNN)
byGaurav Mittal
 
Convolution Neural Network (CNN)
byBasit Rafiq
 
Counter propagation Network
byAkshay Dhole
 

What's hot

PPTX
U-Net (1).pptx
byChangjin Lee
 
PPTX
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
bySimplilearn
 
PPTX
Cnn
byNirthika Rajendran
 
PDF
Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorf...
byEdureka!
 
PPTX
Google net
byBrian Kim
 
PPTX
Batch normalization presentation
byOwin Will
 
PDF
Convolutional Neural Network Models - Deep Learning
byMohamed Loey
 
PPTX
Convolutional Neural Network and Its Applications
byKasun Chinthaka Piyarathna
 
PDF
Generative adversarial networks
by남주 김
 
PDF
Neural Networks: Support Vector machines
byMostafa G. M. Mostafa
 
PPTX
Topic sensitive page rank(review)
byhongs
 
PPT
Back propagation
byNagarajan
 
PPTX
Convolutional neural network
byFerdous ahmed
 
PPTX
Neural network
byRamesh Giri
 
PPT
Unit I & II in Principles of Soft computing
bySivagowry Shathesh
 
PDF
Neural Networks: Self-Organizing Maps (SOM)
byMostafa G. M. Mostafa
 
PDF
Introduction to Generative Adversarial Networks (GANs)
byAppsilon Data Science
 
PDF
Deep Learning
byShaikh Shahzad
 
PPTX
Cnn
byrimshailyas1
 
PPTX
CONVOLUTIONAL NEURAL NETWORK
byMd Rajib Bhuiyan
 
U-Net (1).pptx
byChangjin Lee
 
Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...
bySimplilearn
 
Cnn
byNirthika Rajendran
 
Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorf...
byEdureka!
 
Google net
byBrian Kim
 
Batch normalization presentation
byOwin Will
 
Convolutional Neural Network Models - Deep Learning
byMohamed Loey
 
Convolutional Neural Network and Its Applications
byKasun Chinthaka Piyarathna
 
Generative adversarial networks
by남주 김
 
Neural Networks: Support Vector machines
byMostafa G. M. Mostafa
 
Topic sensitive page rank(review)
byhongs
 
Back propagation
byNagarajan
 
Convolutional neural network
byFerdous ahmed
 
Neural network
byRamesh Giri
 
Unit I & II in Principles of Soft computing
bySivagowry Shathesh
 
Neural Networks: Self-Organizing Maps (SOM)
byMostafa G. M. Mostafa
 
Introduction to Generative Adversarial Networks (GANs)
byAppsilon Data Science
 
Deep Learning
byShaikh Shahzad
 
Cnn
byrimshailyas1
 
CONVOLUTIONAL NEURAL NETWORK
byMd Rajib Bhuiyan
 

Similar to Convolutional Neural Network (CNN)

PDF
CNN
bygeorgejustymirobi1
 
PPT
Deep learning-2017-lecture5 cnn
byAnandShinde47
 
PPT
Deep-Learning-2017-Lecture5CNN.ppt
byEngineeringTamilan
 
PPTX
Deep-Learning-2017-Lecture5CNN.pptx
byDr. Radhey Shyam
 
PPT
Introduction to Deep-Learning-CNN Arch.ppt
bykhandarevaibhav
 
PDF
convolutional neural network and its applications.pdf
bySubhamKumar3239
 
PPT
Deep Learning Techniques like CNN and RNN
bySumaiyaSk
 
PPT
Deep learning-smaller neural network
bysonykhan3
 
PPT
Deep-Learning
byAmnaalia
 
PPT
Deep-Learning-2017-Lecture5CNN.ppt
byAminHa5
 
PPT
Deep-Learning-2017-Lecture5CNN.ppt
bySaadMemon23
 
PPT
Convolutional Neural Networks definicion y otros
byssuserf85a91
 
PPTX
CNN_AH.pptx
byssuserc755f1
 
PPT
Adv.TopicsAICNN.ppt
byZeeshanAhmad449947
 
PPT
Deep-Learning presnetation by someone from the internet
byclash12hero
 
PPT
Deep-Learning-2017-Lecture5CNN.ppt
bysruthiksanalkumar
 
PPT
Deep-Learning-2017-Lecture5CNN.ppt
byarchn4
 
PPTX
Deep-LearningwithVisualExamplesExplaine.pptx
byansarinazish958
 
PDF
Vector-Based Back Propagation Algorithm of.pdf
byNesrine Wagaa
 
PPTX
CNN_AH.pptx
byssuserc755f1
 
CNN
bygeorgejustymirobi1
 
Deep learning-2017-lecture5 cnn
byAnandShinde47
 
Deep-Learning-2017-Lecture5CNN.ppt
byEngineeringTamilan
 
Deep-Learning-2017-Lecture5CNN.pptx
byDr. Radhey Shyam
 
Introduction to Deep-Learning-CNN Arch.ppt
bykhandarevaibhav
 
convolutional neural network and its applications.pdf
bySubhamKumar3239
 
Deep Learning Techniques like CNN and RNN
bySumaiyaSk
 
Deep learning-smaller neural network
bysonykhan3
 
Deep-Learning
byAmnaalia
 
Deep-Learning-2017-Lecture5CNN.ppt
byAminHa5
 
Deep-Learning-2017-Lecture5CNN.ppt
bySaadMemon23
 
Convolutional Neural Networks definicion y otros
byssuserf85a91
 
CNN_AH.pptx
byssuserc755f1
 
Adv.TopicsAICNN.ppt
byZeeshanAhmad449947
 
Deep-Learning presnetation by someone from the internet
byclash12hero
 
Deep-Learning-2017-Lecture5CNN.ppt
bysruthiksanalkumar
 
Deep-Learning-2017-Lecture5CNN.ppt
byarchn4
 
Deep-LearningwithVisualExamplesExplaine.pptx
byansarinazish958
 
Vector-Based Back Propagation Algorithm of.pdf
byNesrine Wagaa
 
CNN_AH.pptx
byssuserc755f1
 

More from Abdulrazak Zakieh

PPTX
Advanced Algorithms: Transform and conquer
byAbdulrazak Zakieh
 
PPTX
The 555 timer
byAbdulrazak Zakieh
 
PPTX
البريد الإلكتروني والبروتوكولات المستخدمة في الإرسال والاستقبال
byAbdulrazak Zakieh
 
PPTX
Hiding data into digital images (Steganography)
byAbdulrazak Zakieh
 
PPTX
Introduction to Deep learning
byAbdulrazak Zakieh
 
PPTX
برنامج محادثة باستخدام الاستدعاء العكسي بلغة الجافا
byAbdulrazak Zakieh
 
PPTX
برنامج محادثة باستخدام الاستدعاء العكسي في استدعاء الطرق البعيدة بلغة الجافا ...
byAbdulrazak Zakieh
 
Advanced Algorithms: Transform and conquer
byAbdulrazak Zakieh
 
The 555 timer
byAbdulrazak Zakieh
 
البريد الإلكتروني والبروتوكولات المستخدمة في الإرسال والاستقبال
byAbdulrazak Zakieh
 
Hiding data into digital images (Steganography)
byAbdulrazak Zakieh
 
Introduction to Deep learning
byAbdulrazak Zakieh
 
برنامج محادثة باستخدام الاستدعاء العكسي بلغة الجافا
byAbdulrazak Zakieh
 
برنامج محادثة باستخدام الاستدعاء العكسي في استدعاء الطرق البعيدة بلغة الجافا ...
byAbdulrazak Zakieh
 

Recently uploaded

DOCX
epigenetic markers in ovarian cancer.docx
byHaarshiniPa
 
PPTX
Keeping the Home Fires Burning - How cesium formate brine has helped to keep ...
byJohn Downs
 
PPTX
Production of RDX Updated.pptx The Bachmann Process
bymudassirabbas6740
 
PPTX
Importance of Asset Register in Maintenance Management
byMaintWiz Technologies Private Limited
 
PPTX
Lean Maintenance: Streamlining Operations for Efficiency & Reliability
byMaintWiz Technologies Private Limited
 
PPTX
Preventive Maintenance for Material Handling Equipment — Complete Guide
byMaintWiz Technologies Private Limited
 
PDF
Albert Pintoy - Specializing In Low-Latency
byAlbert Pintoy
 
PPT
Theory Of Computation-viruses Theory Of Computation-viruses
bySandeepGupta229023
 
PPTX
Inventory Excellence: Achieve Efficient Inventory Management with CMMS
byMaintWiz Technologies Private Limited
 
PPTX
IEC60079-10-1 -Annex F -Hazardous Area Classification -HAC-Annex F.pptx
byasifmirzamakki
 
PDF
ACI 318-2205_American Concrete Institute.pdf
byericaguilerasoto1
 
PPTX
Importance of Instrument Calibration — Improve Accuracy, Reliability & Mainte...
byMaintWiz Technologies Private Limited
 
PPTX
How to Select the Right CMMS Software for Your Organization — A Complete Buye...
byMaintWiz Technologies Private Limited
 
PPTX
How to Create an Effective Monthly Maintenance Plan for Reliable Plant Operat...
byMaintWiz Technologies Private Limited
 
PPTX
The Importance of Maintenance Budgets — Maximize Reliability & Control Costs ...
byMaintWiz Technologies Private Limited
 
PPTX
Plant Performance Strategies: Enhanced Reliability & Operational Efficiency w...
byMaintWiz Technologies Private Limited
 
PPTX
UnrealGameplayAbilitySystemPresentation.pptx
byBenHowenstein
 
PPTX
MECCA Empire – Hotel Shuttle System for the 2026 FIFA World Cup
byay2432
 
PDF
Structural Conservation Appraisal of Indian Monuments Preserving India’s Arch...
byAman Kumar Singh
 
PDF
Lecture -06-Hybrid Policies - Chapter 7- Weeks 6-7.pdf
bysalaarmike
 
epigenetic markers in ovarian cancer.docx
byHaarshiniPa
 
Keeping the Home Fires Burning - How cesium formate brine has helped to keep ...
byJohn Downs
 
Production of RDX Updated.pptx The Bachmann Process
bymudassirabbas6740
 
Importance of Asset Register in Maintenance Management
byMaintWiz Technologies Private Limited
 
Lean Maintenance: Streamlining Operations for Efficiency & Reliability
byMaintWiz Technologies Private Limited
 
Preventive Maintenance for Material Handling Equipment — Complete Guide
byMaintWiz Technologies Private Limited
 
Albert Pintoy - Specializing In Low-Latency
byAlbert Pintoy
 
Theory Of Computation-viruses Theory Of Computation-viruses
bySandeepGupta229023
 
Inventory Excellence: Achieve Efficient Inventory Management with CMMS
byMaintWiz Technologies Private Limited
 
IEC60079-10-1 -Annex F -Hazardous Area Classification -HAC-Annex F.pptx
byasifmirzamakki
 
ACI 318-2205_American Concrete Institute.pdf
byericaguilerasoto1
 
Importance of Instrument Calibration — Improve Accuracy, Reliability & Mainte...
byMaintWiz Technologies Private Limited
 
How to Select the Right CMMS Software for Your Organization — A Complete Buye...
byMaintWiz Technologies Private Limited
 
How to Create an Effective Monthly Maintenance Plan for Reliable Plant Operat...
byMaintWiz Technologies Private Limited
 
The Importance of Maintenance Budgets — Maximize Reliability & Control Costs ...
byMaintWiz Technologies Private Limited
 
Plant Performance Strategies: Enhanced Reliability & Operational Efficiency w...
byMaintWiz Technologies Private Limited
 
UnrealGameplayAbilitySystemPresentation.pptx
byBenHowenstein
 
MECCA Empire – Hotel Shuttle System for the 2026 FIFA World Cup
byay2432
 
Structural Conservation Appraisal of Indian Monuments Preserving India’s Arch...
byAman Kumar Singh
 
Lecture -06-Hybrid Policies - Chapter 7- Weeks 6-7.pdf
bysalaarmike
 

Convolutional Neural Network (CNN)

  • 1.
    Convolutional Neural Networks(CNN) BY: ABDULRAZAK ZAKIEH ARTIFICIAL INTELLIGENCE CLASS, 2020-2021 LECTURER: PROF. ÖZLEM AKTAŞ
  • 2.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 2
  • 3.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 3
  • 4.
    CNNs Applicatios ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM)4 level 2 autonomous driving assistance TOGG COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings Jordi Laguarta, Ferran Hueto, Brian Subirana 30 SEP 2020
  • 5.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 5
  • 6.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 6 Y output
  • 7.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 7 Fully Connected Fully Connected
  • 8.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 8
  • 9.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 9 ???????
  • 10.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 10 ???????
  • 11.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 11 ??????? Does not capture the “natural” invariances we expect in images (translation, scale)
  • 12.
    Neural Networks (revision) ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 12 256 * 256 RGB image – 10 outputs – 1,966,080 Weights Over fitting the data
  • 13.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 13
  • 14.
    Filters – HorizontalEdges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 15 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 5 × 5 3 × 3 1 1 1 0 0 0 -1 -1 -1
  • 15.
    Filters – HorizontalEdges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 16 1 1 1 0 0 0 -1 -1 -1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 1 1 1 000 -1-1-1 39 1 1 1 000 -1-1-1 39 -70 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 39 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 135 * = 5 × 5 3 × 3 3 × 3
  • 16.
    Filters – VerticalEdges Detector ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 17 -1 0 1 -1 0 1 -1 0 1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 1 1 1 000 -1-1-1 160 1 1 1 000 -1-1-1 160 0 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 1 1 1 000 -1-1-1 160 0 -59160 0 -59 65 160 0 -59 65 67 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 -134 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 -134 55 1 1 1 000 -1-1-1 160 0 -59 65 67 -31 134 55 -39 * = 5 × 5 3 × 3 3 × 3
  • 17.
  • 18.
    Convolutional Neural Networks(CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 19 Which filter to use?
  • 19.
    Convolutional Neural Networks(CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 20 y = z * w
  • 20.
    Convolutional Neural Networks(CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 21 “learn” the right filter(s) for the specified task
  • 21.
    Content CNN Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 22
  • 22.
    Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM)23 -1 0 1 -1 0 1 -1 0 1 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 160 0 -59 65 67 -31 134 55 -39 * = 5 × 5 3 × 33 × 3
  • 23.
    Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM)24 “zero pad” the input image
  • 24.
    Padding ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM)25 -1 0 1 -1 0 1 -1 0 1 0 0 0 0 0 0 0 0 10 80 90 10 24 0 0 41 12 53 13 53 0 0 11 51 79 120 86 0 0 68 13 53 10 15 0 0 45 23 126 12 12 0 0 0 0 0 0 0 0 92 92 -79 -66 -23 143 160 0 -59 -143 76 65 67 -31 -143 87 134 55 -39 -142 36 66 -14 -152 -22 * = 5 × 5 5 × 5 3 × 3 7 × 7 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12
  • 25.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 26
  • 26.
    Stride Convolution ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 27 1 1 1 0 0 0 -1 -1 -1 10 80 90 10 24 23 73 41 12 53 13 53 12 82 11 51 79 120 86 57 23 68 13 53 10 15 12 64 45 23 126 12 12 86 12 72 82 45 65 95 81 51 46 97 71 123 69 8 62 1 1 1 000 -1-1-1 39 1 1 1 000 -1-1-1 39 -70 1 1 1 000 -1-1-11 1 1 000 -1-1-1 1 1 1 000 -1-1-1 39 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 1 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 891 1 1 000 -1-1-1 39 -70 -161 -28 2 41 -53 89 135 * = 7 × 7 3 × 3 3 × 3 Stride = 2
  • 27.
    Width and Heightof the Output ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 28 Filter Size: f × f Padding: p Stride: s Input width: 𝑛 𝑤 Input height: 𝑛ℎ [ 𝑛 𝑤 + 2𝑝 − 𝑓 𝑠 + 1 , 𝑛ℎ + 2𝑝 − 𝑓 𝑠 + 1]
  • 28.
    Valid and SameConvolution ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 29 Same Valid 50 * 50 n * n SameCONV s =1 p=(f –1) / 2 50 * 50 50 * 50 CONV 30 * 30
  • 29.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Example ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 30
  • 30.
    Convolution over Volume ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 31 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 5 × 5 × 3 3 × 3 × 3 = 3939 -7039 -70 -16139 -70 -161 -28 39 -70 -161 -28 2 39 -70 -161 -28 2 41 39 -70 -161 -28 2 41 -53 39 -70 -161 -28 2 41 -53 89 39 -70 -161 -28 2 41 -53 89 135 R-G-B 𝒏 𝒘 × 𝒏 𝒉 × 𝒏 𝒄 𝒇 𝒉 × 𝒇 𝒘 × 𝒏 𝒄 𝒏 𝒘 𝟏 × 𝒏 𝒉 𝟏 3 × 3
  • 31.
    Convolution over Volume ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 32 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 R-G-B 5 × 5 × 3 3 × 3 × 3 = 39 -70 -161 -28 2 41 -53 89 135 -1 0 1 -1 0 1 -1 0 1 * -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 3 × 3 × 3 12 53 10 81 24 -11 61 -12 -53 39 -70 -161 -28 2 41 -53 89 135 12 53 10 81 24 -11 61 -12 -53 3 × 3 3 × 3 3 × 3 × 2
  • 32.
    Convolution over Volume ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 33 1 1 1 0 0 0 -1 -1 -1 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 * 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 120 14 124 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 134 15 45 23 126 12 12 1 1 1 0 0 0 -1 -1 -1 1 1 1 0 0 0 -1 -1 -1 = -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 -1 0 1 39 -70 -161 -28 2 41 -53 89 135 12 53 10 81 24 -11 61 -12 -53 CONV 3 × 3 × 3 #2 5 × 5 × 3 3 × 3 × 2 CONV f = 3 5 × 5 × 3 3 × 3 × 2
  • 33.
    Convolution over Volume ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 34 Number of channels for each filter? 3, 4, 5 Number of filter? 3, 4, 5
  • 34.
    Convolutional Neural Networks(CNN) ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 35 Mathematical: It’s correlation, not convolution
  • 35.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Example ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 36
  • 36.
    Layer Types inCNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 37 Max-PoolingCONV Fully Connected
  • 37.
    Layer Types inCNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 38 Max-PoolingCONV Fully Connected 𝑧𝑖+1 = 𝑧𝑖 ∗ 𝑊𝑖
  • 38.
    Layer Types inCNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 39 Max-PoolingCONV 𝑧𝑖+1 = 𝑧𝑖 ∗ 𝑊𝑖 10 80 90 10 24 41 12 53 13 53 11 51 79 120 86 68 13 53 10 15 45 23 126 12 12 9090 12090 120 12090 120 120 79 90 120 120 79 120 90 120 120 79 120 120 90 120 120 79 120 120 126 90 120 120 79 120 120 126 126 90 120 120 79 120 120 126 126 126 𝑓 = 3 S = 1
  • 39.
    Layer Types inCNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 40 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 8080 9080 90 68 80 90 68 86 𝑓 = 2 S = 2
  • 40.
    74 67 82 97 7414 17 12 66 27 42 67 82 34 75 6 17 78 63 97 Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 41 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 80 90 68 86 𝑓 = 2 S = 2 𝑀𝐴𝑋 𝑃𝑂𝑂𝐿
  • 41.
    74 67 82 97 7414 17 12 66 27 42 67 82 34 75 6 17 78 63 97 Layer Types in CNN ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 42 Max-Pooling 10 80 90 24 41 12 53 53 11 51 79 86 68 13 53 15 80 90 68 86 𝑓 = 2 S = 2 𝑀𝐴𝑋 𝑃𝑂𝑂𝐿 Fully Connected
  • 42.
    Content CNNs Applications Neural Networks(revision) Convolution Neural Network Padding Stride Convolution Convolution Over Volume Layer Types in CNN CNN Examples ABDULRAZAK ZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 43
  • 43.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 44 LeNet-5 ~ 60𝐾
  • 44.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 45 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet ~ 60𝑀
  • 45.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 46 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet
  • 46.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 47 AlexNet [Krizhevskyet al., 2012. ImageNet Classification with Deep Convolutional Neural Networks] AlexNet
  • 47.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 48 Simonyon & Zisserman 2015 (~138 M) VGG - 16
  • 48.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 49 Deep Residual Learningfor ImageRecognition (He.K et al. 2015) ResNet
  • 49.
    CNN Examples ABDULRAZAK ZAKIEH(ABDLARZAK.ZK@GMAIL.COM) 50 Going Deeper with Convolutions (Christian Szegedy et. al. 2015) GoogLeNet
  • 50.
    Thanks for listening ABDULRAZAKZAKIEH (ABDLARZAK.ZK@GMAIL.COM) 51