This document provides an overview of deep machine learning and its applications. It introduces Mario Cho and his experience with image recognition, medical data processing, and open source software development. The document then discusses neural networks, deep learning techniques, and how deep learning can be applied to areas like image recognition, natural language processing, and autonomous vehicles. Examples of deep learning applications include image captioning, machine translation, and generating synthetic images from sketches.

1. 인공지능(딥러닝)
이해와 응용
Deep Machine Learning &
Applications
KOSS Lab. 2기
Mario Cho (조만석)
hephaex@gmail.com

2. Contents
• 기계학습 (NNs)
• Neural Networks
• 딥러닝 기술
• 활용방안

3. Mario Cho
Development Experience
◆ Image Recognition using Neural Network
◆ Bio-Medical Data Processing
◆ Human Brain Mapping on High Performance
Computing
◆ Medical Image Reconstruction
(Computer Tomography)
◆ Enterprise System
◆ Open Source Software Developer
Open Source Software Developer
◆ OPNFV (NFV&SDN) & OpenStack
◆ Machine Learning (TensorFlow)
Book
◆ Unix V6 Kernel Korea Open Source Software Lab.
Mario Cho
hephaex@gmail.com

4. Today’s information
* http://www.cray.com/Assets/Images/urika/edge/analytics-infographic.html

5. The Future of Jobs
“The Fourth Industrial Revolution, which
includes developments in previously
disjointed fields such as
artificial intelligence & machine-learning,
robotics, nanotechnology, 3-D printing,
and genetics & biotechnology,
will cause widespread disruption not only
to business models but also to labor
market over the next five years, with
enormous change predicted in the skill
sets needed to thrive in the new
landscape.”

6. What is the Machine Learning ?
• Field of Computer Science that evolved from the
study of pattern recognition and computational
learning theory into Artificial Intelligence.
• Its goal is to give computers the ability to learn
without being explicitly programmed.
• For this purpose, Machine Learning uses
mathematical / statistical techniques to construct
models from a set of observed data rather than
have specific set of instructions entered by the
user that define the model for that set of data.

7. Required of New type of Computing
understand information,
to learn,
to reason,
and act upon it

8. Traditional learning vs Deep Machine Learning
Eiffel Tower
Eiffel Tower
RAW data
RAW data
Deep
Learning
Network
Feature
Extraction
Vectored Classification
Traditional Learning
Deep Learning

9. Neuron

10. Human Intelligence

11. Brain Map

12. hippocampus

13. Neural network vs Learning network
Neural Network Deep Learning Network

14. Neural Network
W1
W2
W3
f(x)
1.4
-2.5
-0.06

15. Neural Network
2.7
-8.6
0.002
f(x)
1.4
-2.5
-0.06
x = -0.06×2.7 + 2.5×8.6 + 1.4×0.002 = 21.34

16. Neural Network : x1 XNOR x2
+1
x1
x2
+1
a1(2)
a2(2)
a1(3)
-30
20
20
10
-20
-10
-10
10
-20
x1 x2 a1(2) a2(2) a1(3)
0 0 0 1 1
0 1 0 0 0
1 0 0 0 0
1 1 1 0 0
Output
10

17. Multi-layer Neural Networks
Train this layer first

18. Multi-layer Neural Networks
Train this layer first
then this layer
then this layer
then this layer
finally this layer

19. Tic Tac Toc

20. AlphaGo
RAW
data
Layer1
Policy network & value network
Selection
Expansions
Evaluation
Backup
~ Layer13

21. Deep learning - CNN

22. Image recognition in Google Map
* Source: Oriol Vinyals – Research Scientistat Google Brain

23. Deep Learning Hello World == MNIST

24. MNIST (predict number of image)

25. CNN (convolution neural network) training

26. MNIST

27. Old Character Recognition

28. Human-Level Object Recognition
• ImageNet
• Large-Scale Visual Recognition Challenge
–Image Classification / Localization
–1.2M labeled images, 1000 classes
–Convolutional Neural Networks (CNNs)
has been dominating the contest since..
– 2012 non-CNN: 26.2% (top-5 error)
– 2012: (Hinton, AlexNet)15.3%
– 2013: (Clarifai) 11.2%
– 2014: (Google, GoogLeNet) 6.7%
– 2015: (Google) 4.9%
– Beyond human-level performance

29. Hierarchical Representation of Deep Learning
* Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations
with Convolutional Deep Belief Networks”.

30. * Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations
with Convolutional Deep Belief Networks”.
Deep Learning (object parts)

31. Face extraction method

32. Face recognition data- sets?

33. Human-Level Face Recognition
• Convolutional neural networks based
face recognition system is dominant
• 99.15% face verification accuracy on
LFW dataset in DeepID2 (2014)
– Beyond human-level recognition
Source: Taigman et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR’14

34. Image Recognition
* Source: Oriol Vinyals – Research Scientistat Google Brain

35. Object Classification and Detection

36. How to the Object recognition ?

37. Image Caption Generation

38. Language Generating
* Source: Oriol Vinyals – Research Scientistat Google Brain

39. Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]

40. Scene Parsing
[Farabet et al. ICML 2012, PAMI 2013]

41. Auto pilot car

42. Automatic Colorization of Black and White Images

43. Generate sounds on old-movies

44. Automatic Machine Translation
• Automatic Translation of Text.
• Automatic Translation of Images.

45. Automatic Handwriting Generation

46. Text Generation

47. create stylized images from rough sketches.

48. Inspirer Humanity

49. Thanks you!
Q&A