2019 SK엔카 테크캠프에서 딥러닝을 주제로 발표한 자료입니다.

1. SK

2. Deep Learning

3. Deep Learning
y = f(x)

4. Deep Learning
y = f(x)

5. Deep Learning
y = f(x)

6. Deep Learning
y = f(x)
Model

7. Matrix Product

8. Matrix Product
[
6 2
1 4]
×
[
3 7
4 5]

9. Matrix Product
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 ?
? ?]

10. Matrix Product
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
? ? ]

11. Matrix Product
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 ? ]

12. Matrix Product
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 27]

13. Prediction

14. Prediction
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 27]
input Prediction

15. Prediction
[
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 27]
input PredictionModel

16. Deep Learning
y = f(x)

17. Prediction
[
6 2
1 4]
×
[
3 7
4 5]
× ⋯ ×
[
4 6
1 8]
=
[
26 52
19 27]
input PredictionModel

18. Prediction
[
6 2
1 4]
×
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
⋯
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
⋮ ⋮
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
⋯
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
−0.2 −0.8 0.6
0.1 0.9 0.3
0.9 −0.4 −0.1
0.1 0.3 0.1
0.6 0.2 0.4
0.1 0.1 0.8
=
[
26 52
19 27]
input Prediction
Model

19. Prediction
input
Prediction
Model

20. Prediction
input PredictionModel

21. Prediction
input PredictionModel

22. Prediction - Convolutional Neural Network

23. Prediction - Convolutional Neural Network

24. Prediction - Recurrent Neural Network

25. Prediction - Recurrent Neural Network

26. Training

27. y = f(x)
Training

28. y′ = f(x)
Training

29. y′ = f(x)
Prediction Value
Training

30. [
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 27]
input PredictionModel
Training

31. y ≠ y′
Answer Prediction Value
Training

32. y ≠ y′
Label Prediction Value
Training

33. [
6 2
1 4]
×
[
3 7
4 5]
=
[
26 52
19 27]
input PredictionModel
Training

34. [
6 2
1 4]
×
[
4 6
4 4]
=
[
32 44
20 22]
input PredictionModel
Training

35. [
6 2
1 4]
×
[
5 5
3 2]
=
[
36 34
17 13]
input PredictionModel
Training

36. [
6 2
1 4]
×
[
6 4
2 2]
=
[
40 28
14 12]
input PredictionModel
Training

37. y ≃ y′
Label Prediction Value
Training

38. Loss function

39. Training
loss = L(y, y′)

40. Prediction
[
26 52
19 27] [
62 18
32 42]
yy′

41. [
62 18
32 42][
26 52
19 27]
Loss function
L(y, y′) = ?
yy′

42. [
62 18
32 42]
−
[
26 52
19 27]
=
[
36 −34
13 −15]
= 0
Loss function
L(y, y′) =
∑
y − y′
y y′

43. = (62 − 26)2
+ (18 − 52)2
+ (32 − 19)2
+ (42 − 27)2
Loss function
L(y, y′) = ∥ y − y′ ∥
= 2846
≃ 53.3

44. =
1
4
(62 − 26)2
+ (18 − 52)2
+ (32 − 19)2
+ (42 − 27)2
Loss function
L(y, y′) =
1
n
∥ y − y′ ∥
=
1
4
2846
≃ 13.3

45. Mean Squared Error

46. Mean Squared Error
Mean Absolute Error
Cross Entropy Error
Possion Error
⋮

47. Training
Model
[
w0 w1
w2 w3] L(y, y′)
?

48. Loss ?

49. –wikipedia (https://ko.wikipedia.org/wiki/ )
“ ”

50. “ Loss ”

51. δL
δwi
wi L(y, y′)
Training

52. [
w00 w01
w02 w03]
×
[
w10 w11
w12 w13]
3
∑
j=0
δL
δw1j
δw1j
δw0i
Training

53. [
w00 w01
w02 w03]
×
[
w10 w11
w12 w13]
×
[
w20 w21
w22 w23]
3
∑
k=0
3
∑
j=0
δL
δw2k
δw2k
δw1j
δw1j
δw0i
Training

54. Backpropagation

55. Optimization

56. Update weight
wi = wi − γ
δL
δwi

57. Update weight
wi = wi − γ
δL
δwi

58. Update weight
wi = wi − γ
δL
δwi
Learning rate

59. Stochastic Gradient Descent
Momentum
RMS Propagation
Adam
⋮

60. y ≃ y′
Label Prediction Value
Training

62. Automation

66. !
2017.8~

67. One-hot Encoding

68. One-hot Encoding
[1 0 0 0 0]
[0 1 0 0 0]
[0 0 1 0 0]
[0 0 0 1 0]
[0 0 0 0 1]

70. X
One-hot Encoding
[1 0]
[0 1]

71. =
[1 0]
or
[0 1]
input PredictionModel
[image] ×

72. 0~9
One-hot Encoding
[1 ⋯ 0]
[0 ⋯ 1]~
50

73. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50

74. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0

75. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8

76. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8

77. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8
6

78. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8
6
7

79. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8
6
7
2

80. input Model
[image] × =
1 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
0 ⋯ 0
Prediction
50
0
8
6
7
2
3

83. ….
3 …

87. !
(2018.7~)

88. 10

89. So Easy

90. One-hot Encoding
[1 0 0 0 0 0 0 0 0 0]
[0 1 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 0 0 1]
⋮

91. input
Prediction
Model
[image] × =
[1 0 ⋯ 0 0]
⋮
[0 0 ⋯ 0 1]

92. 3 99%

93. ?

95. One-hot Encoding
[0 0 0 0 0 0 0 0 0 0 1]

96. “ ?”

97. “ ”

99. input PredictionModel
[image] × = 0 ∼ 1

100. idea

101. Deployment

107. Tensorflow serving

116. Object Detection

118. YOLO
FastRCNN
FasterRCNN
SSD

119. ‘ …!’

120. ‘ / ?’

126. ‘ ?’

128. ‘ ?’

129. Prediction - Convolutional Neural Network

130. Transfer Learning

131. 2,000

132. 99.73%
99.81%
99.73% x 99.81% = 99.48%

133. Amazing!