This document provides an overview of deep learning for beginners. It introduces key concepts in deep learning like machine learning, neural networks, linear regression, and gradient descent. Linear regression is used as an example to explain how machine learning works - by generating a hypothesis function, calculating a cost/loss function, and using gradient descent to minimize the cost/loss and train the model to best fit the data.

1. Deep Learning
초보 개발자를 위한 개요

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

3. Personal Computer

4. Internet

5. Mobile

6. Next?

7. AI
Machine Learning
Deep LearningDeep LearningDeep LearningDeep 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]
HypothesisHypothesisHypothesisHypothesis
Cost/LossCost/LossCost/LossCost/Loss
Gradient DescentGradient DescentGradient DescentGradient Descent
X = 4 일때 Y = 4 가 맞음 ?
W, B

23. Thank you