This presentation serves as a very basic introduction to Deep Learning and Deep Neural Networks, and how does Deep Learning fits in Machine Learning and Artificial Intelligence arena. At the end of the presentation, we list some MATLAB commands that are required to use Deep Learning. MATLAB file can be downloaded from this link: https://esgsa-my.sharepoint.com/:u:/p/mohammad_alkhodary/EXmto3ykTC5PtrULDqpuG6MBVbtYZ68YIIXxDtu5MTqHwQ?e=y8dodf

1. Brief Introduction to Deep Learning for
Object Recognition Using MATLAB
Mohammad Tamim Alkhodary
Mar 7, 2018
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2. Objectives
• Get Inspired by new innovation and the transformation
of the technology
• Understand the concept of Deep Learning and it’s
relation to machine learning and Artificial Intelligence
• Get stared with Deep Neural Network
2

3. Contents
 Introduction to AI and Machine Learning
 What is Deep Learning
 How does deep learning works
 Applications of Deep Learning
 MATLAB Example
3

4. Artificial
Intelligence
(AI)
4
• AI will 'transform or
destroy' society
• AI could be the "worst
event in the history of
our civilization" unless
society finds a way to
control its developments

5. 5

6. 6

7. What is Deep Learning?
• Deep learning is a Machine learning technique that learn
features and tasks directly from data
• Date could be
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images, text, sounds, or data points

8. Example of Deep Learning
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9. N to N learning
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10. Why now?
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11. Why now?
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12. Steps for Full Deep Learning
Netorwk
• Objects
• Model
Building
• Data
• Different
Senario
Training
• right away
Implement
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13. Deep Neural Network
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14. Neural Network
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15. Neural Network
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16. 16
The breakthrough
$500,000,000

17. 17

18. Applications of Deep Learning
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19. Applications of Deep Learning
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20. Convolutional Neural Network
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21. Importance of GPU’s
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22. Image recognition Application
• Self Driving Car
• Health: cancer cell prediction
• Quality control and defected item recognition
• Counting
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23. 23

24. 24

25. 25

26. How to build Deep Neural Network
Decide on the
application
Build the
feature
extraction
layers (Deep
layers)
Train the
network
Export
Network cof
Set-up Stage

27. How to build Deep Neural Network
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Import DNN
conf. data
adjust your new
data according
to parameters
start using it
Implementation Stage

28. Alex Net 2012
• In MATLAB Add-ins search for Pre-trained Network
• Install AlexNet
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29. Not always accurate!!
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30. Needed Packages for MATLAB
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• MATLAB Support Package for USB Webcams
• AlexNet pre-trained network
• Statistics and Machine Learning Toolbox

31. Basic MATLAB commands
• net = Alexnet % network ojects
• Lyers = net.Layers
• Input layer = Lyers(1) % first layer input layer
• ClassLayer = ly(end) % is the class name
• CategoryNames = ClassLayer.ClassNames
• [pred, percent] = classify(net, ImagePointer)
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32. Transfer Learning checklist
A network
Training Data
Algorithm options
Batch size
Max iteration
Learning Rate
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33. Resources
• MATLAB Machine learning help document
• Machine learning for self-driving cars MIT-open course
• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton.
"ImageNet Classification with Deep Convolutional Neural
Networks." Advances in neural information processing
systems. 2012.
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34. Thank you,
Q&A
34

35. How to train a network
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36. 36
imds = imageDatastore('file*.jpg’)
img = readimage(imds,07)
imageDatastore('file*.jpg’)
imageDatastore(‘foldername’,’include sub
folders’, true)