오픈소스로 시작하는 인공지능 실습

1. 오픈소스로 시작하는 인공지능 실습 Artificial Intelligence practice starting with open source 중앙대학교 의료보안연구소 Mario Cho (조만석) hephaex@gmail.com

2. 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 ◆ OPNFV (NFV&SDN) & OpenStack ◆ Machine Learning (TensorFlow, Torch, Leaf) Cognitive Artificial Intelligence for Medicine ◆ Machine Learning ◆ Medical Informatics of oncology Book ◆ Unix V6 Kernel Chung-Ang University Mario Cho hephaex@gmail.com

3. 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.

4. Neural network vs Learning network Neural Network Deep Learning Network

5. Neural Network as a Computational Graph • In Most Machine Learning Frameworks, • Neural Network is conceptualized as a Computational Graph • The simple form of Computational Graph, • Directed Acyclic Graph consist Data Nodes and Operator Nodes Y = x1 * x2 Z = x3 – y Data node Opeator node

6. Tensorflow Computational Graph Tensor (다차원행렬) Tensor Tensor 곱셈 덧셈 함수 Tensor: 3차 이상 다차원 행렬

7. Single layer perceptron Affine ReLUX W b h1 C

8. Multi layer perceptron X W1 b1 h1Affine a1 W2 b2 h2Affine ReLU ReLU a2 W3 b3 h3Affine Softmax t Cross Entropy prob loss

9. What is a neural network? Yes/No (Mug or not?) Data (image) ! x1 ∈!5 ,!x2 ∈!5 x2 =(W1 ×x1 )+ x3 =(W2 ×x2 )+ x1 x2 x3 x4 x5 W4W3W2W1

10. Deep learning : CNN

11. Make predictions on data

12. Deep Learning Framework comparison 출처: Getting Started with Dep Learning https://svds.com/getting-started-deep-learning/

13. GPU

14. Tensor Operation in GPU

15. Tensor Core : NVIDIA Volta

16. NVIDIA Volta Architecture

17. Comparison of NVIDIA GPUs

18. Nvidia GPU Roadmap

19. AMD GPU road map

20. Tensorflow Processing Unit (TPU)

21. AlphaGo Gen1

22. Machine Learning Farm

23. Why is Deep Learning taking off? Engine Fuel Large neural networks Labeled data (x,y pairs)

24. Google S/W Projects

25. History of Deep Learning Framework 2010 2013 2014 2015 2016 2017 (Nov.) (Dec.) (Jul.) (Jun.) On GitHub (Debut: Apr. ‘2015) (Oct.) (Jun.) (Nov.) (Jan.) (Apr.) (Mar.)

26. Google Tensorflow

27. I. Setup Virtual Environment • Virtual Box 5.1 Download & install. • https://www.virtualbox.org

28. I. Setup Virtual Environment • VirtualBox 5.1.22 for Windows hosts x86/amd64 • VirtualBox 5.1.22 for OS X hosts amd64

29. II. Operating System: download • http://releases.ubuntu.com/ • http://releases.ubuntu.com/xenial/

30. II. Operating System: download • https://launchpad.net/ubuntu/+mirror/ftp.daum.net-release

31. II. Operating System: virtual box setup

39. II. Operating System: ready to install

40. II. Operating System: install

64. III. Setting Network • $ sudo nano /etc/network/interfaces – 네트워크 장치 정보를 입력하고 CTRL+X로 저장 • $ sudo reboot

65. III. SSH server install • 설치를 안했을 경우 새로 설치 • $ sudo apt-get install openssh-server • 검증 verify • $ sudo service ssh status

66. III. install local terminal • http://www.putty.org 에 접속해서

67. III. install local terminal • http://www.putty.org 에 접속해서

68. III. install local terminal

71. III. Open local terminal using putty or term • Windows 환경: putty 를 설치하고, 창을 열어 192.168.56.10 으로 접속합니다. • OSX 환경: 터미널을 열어 $ ssh 192.168.56.10 –l ubuntu 로 접속합니다.

72. III. Repository update • $ sudo apt-get update && sudo apt-get dist-upgrade

73. IV. Install docker • $ wget -qO- https://get.docker.com/ | sh • $ sudo usermod -aG docker ubuntu

74. V. Execute dev. Based on web. • $ $ docker run -it -p 8888:8888 hephaex/tensorflow:1.1.0 – 텐서 플로우 1.1.0 버전과 표준 사용 예제가 설치된 도커 이미지 • $ docker run -it -p 8888:8888 hephaex/tensorflow:etri – 텐서 플로우 1.1.0 버전과 실습에 사용된 예제가 설치된 도커 이미지

75. V. Execute dev. Based on web. • 웹브라우져 (IE, Chrome, Sapari, FireFox, , , etc) • http://192.168.56.10:8888 • 패드워드 : tensorflow

76. Vi. Tutorial #1: hello world

77. Vi. Tutorial #1: install pip package

78. Vi. Tutorial #1 : Hello TensorFlow

79. Vi. Tutorial #1 : add operation

80. Vi. Tutorial #1 : loop

81. iX. Tutorial #2 matrix multiplication

84. IX. Tutorial #3 word2vector

90. X. Tutorial #4 data representation

98. XI. Tutorial #5 Linear regression

102. XII. Tutorial #6 MNIST: Image recognition in Google Map * Source: Oriol Vinyals – Research Scientist at Google Brain

103. XII. Tutorial #6 MNIST

104. XII. Tutorial #6 MNIST: data set

112. Challenges Computing

113. Thanks you! Q&A

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오픈소스로 시작하는 인공지능 실습