자세한 설명은 https://goo.gl/QKWaWV

1. Deep Learning
초보 개발자를 위한 개요

2. https://factordaily.com/india-government-ai-analytics/

3. Personal Computer

4. Internet

5. Mobile

6. Next?

7. AI
Machine Learning
Deep Learning

8. Spam Filter
• Rule based -> Explicit
• Train
• Neural Network
• Deep, Wide

9. Linear Regression

10. X = [1, 2, 3]
Y = [1, 2, 3]
X = 4 일때 Y = ?

11. THE MATHEMATICAL
WAY OF THINKING
Science 15 Nov 1940:

12. Y = X * W + B
W: weight, B: bias

13. Machine Learning
• Hypothesis
• cost / loss
• Gradient descent
• Train
• Test

14. Hypothesis
H(X) = X*W + B
X = [1, 2, 3] Y = [1, 2, 3]
W=2 , B = 0
H(X) = [H(1), H(2), H(3)] = [2, 4, 6]

15. Cost / Loss function
H(X) = [H(1), H(2), H(3)] = [2, 4, 6]
Y = [1, 2, 3]
euclidean distance

16. Cost / Loss function
X = [1, 2, 3] Y = [1, 2, 3]
W= 2, B=0 H = [2, 4, 6] cost(2) = 14
W= 1.5, B=0 H = [1.5, 3, 4.5] cost(1.5) = 1.166
W= 1, B=0 H = [1, 2, 3] cost(1) = 0

17. Cost / Loss function
X = [1, 2, 3] Y = [1, 2, 3] B = 0

18. Gradient Descent
W= 2, B=0 H = [2, 4, 6] cost(2) = 14
W= 1.5, B=0 H = [1.5, 3, 4.5] cost(1.5) = 1.166
W= 1, B=0 H = [1, 2, 3] cost(1) = 0
Minimize Cost function

19. Gradient Descent
cost(w)
w
w=2
w=1.5
w=1

20. Gradient Descent

21. Gradient Descent
cost(w)
w
w=2
w=1.5
w=1
w=1.2

22. Train & Test
X = [1, 2, 3] Y = [1, 2, 3]
Hypothesis
Cost/Loss
Gradient Descent
X = 4 일때 Y = 4 가 맞음?
W, B

23. Thank you