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# 오픈소스로 시작하는 인공지능 실습

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대표적인 인공지능 프레임 워크인 텐서플로우를 설치부터 실습까지 설명하는 자료입니다.
인공지능에 관심 있는 분들이 처음부터 시작할 수 있는 마중물이 되었으면 하는 바램입니다.
본 자료는 ETRI 인공 지능 실습에 사용되었습니다.

저작물에 저작권은 원 사용자에게 있습니다.

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### 오픈소스로 시작하는 인공지능 실습

1. 1. 오픈소스로 시작하는 인공지능 실습 Artificial Intelligence practice starting with open source 중앙대학교 의료보안연구소 Mario Cho (조만석) hephaex@gmail.com
2. 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. 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. 4. Neural network vs Learning network Neural Network Deep Learning Network
5. 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. 6. Tensorflow Computational Graph Tensor (다차원행렬) Tensor Tensor 곱셈 덧셈 함수 Tensor: 3차 이상 다차원 행렬
7. 7. Single layer perceptron Affine ReLUX W b h1 C
8. 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. 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. 10. Deep learning : CNN
11. 11. Make predictions on data
12. 12. Deep Learning Framework comparison 출처: Getting Started with Dep Learning https://svds.com/getting-started-deep-learning/
13. 13. GPU
14. 14. Tensor Operation in GPU
15. 15. Tensor Core : NVIDIA Volta
16. 16. NVIDIA Volta Architecture
17. 17. Comparison of NVIDIA GPUs
19. 19. AMD GPU road map
20. 20. Tensorflow Processing Unit (TPU)
21. 21. AlphaGo Gen1
22. 22. Machine Learning Farm
23. 23. Why is Deep Learning taking off? Engine Fuel Large neural networks Labeled data (x,y pairs)
25. 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.)
27. 27. I. Setup Virtual Environment • Virtual Box 5.1 Download & install. • https://www.virtualbox.org
28. 28. I. Setup Virtual Environment • VirtualBox 5.1.22 for Windows hosts x86/amd64 • VirtualBox 5.1.22 for OS X hosts amd64
31. 31. II. Operating System: virtual box setup
32. 32. II. Operating System: virtual box setup
33. 33. II. Operating System: virtual box setup
34. 34. II. Operating System: virtual box setup
35. 35. II. Operating System: virtual box setup
36. 36. II. Operating System: virtual box setup
37. 37. II. Operating System: virtual box setup
38. 38. II. Operating System: virtual box setup
39. 39. II. Operating System: ready to install
40. 40. II. Operating System: install
41. 41. II. Operating System: install
42. 42. II. Operating System: install
43. 43. II. Operating System: install
44. 44. II. Operating System: install
45. 45. II. Operating System: install
46. 46. II. Operating System: install
47. 47. II. Operating System: install
48. 48. II. Operating System: install
49. 49. II. Operating System: install
50. 50. II. Operating System: install
51. 51. II. Operating System: install
52. 52. II. Operating System: install
53. 53. II. Operating System: install
54. 54. II. Operating System: install
55. 55. II. Operating System: install
56. 56. II. Operating System: install
57. 57. II. Operating System: install
58. 58. II. Operating System: install
59. 59. II. Operating System: install
60. 60. II. Operating System: install
61. 61. II. Operating System: install
62. 62. III. Setting Network • \$ sudo nano /etc/network/interfaces  네트워크 장치 정보를 입력하고 CTRL+X로 저장 • \$ sudo reboot
63. 63. III. SSH server install • 설치를 안했을 경우 새로 설치 • \$ sudo apt-get install openssh-server • 검증 verify • \$ sudo service ssh status
64. 64. III. install local terminal • http://www.putty.org 에 접속해서
65. 65. III. install local terminal • http://www.putty.org 에 접속해서
66. 66. III. install local terminal
67. 67. III. install local terminal
68. 68. III. install local terminal
69. 69. 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.
70. 70. III. Repository update • \$ sudo apt-get update && sudo apt-get dist-upgrade
71. 71. IV. Install docker • \$ wget -qO- https://get.docker.com/ | sh • \$ sudo usermod -aG docker ubuntu
72. 72. 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
73. 73. V. Execute dev. Based on web. • 웹브라우져 (IE, Chrome, Sapari, FireFox, , , etc) • http://192.168.56.10:8888 • 패드워드 : tensorflow
74. 74. Vi. Tutorial #1: hello world
75. 75. Vi. Tutorial #1: install pip package
76. 76. Vi. Tutorial #1 : Hello TensorFlow
77. 77. Vi. Tutorial #1 : add operation
78. 78. Vi. Tutorial #1 : loop
79. 79. iX. Tutorial #2 matrix multiplication
80. 80. iX. Tutorial #2 matrix multiplication
81. 81. iX. Tutorial #2 matrix multiplication
82. 82. IX. Tutorial #3 word2vector
83. 83. IX. Tutorial #3 word2vector
84. 84. IX. Tutorial #3 word2vector
85. 85. IX. Tutorial #3 word2vector
86. 86. IX. Tutorial #3 word2vector
87. 87. IX. Tutorial #3 word2vector
88. 88. X. Tutorial #4 data representation
89. 89. X. Tutorial #4 data representation
90. 90. X. Tutorial #4 data representation
91. 91. X. Tutorial #4 data representation
92. 92. X. Tutorial #4 data representation
93. 93. X. Tutorial #4 data representation
94. 94. X. Tutorial #4 data representation
95. 95. X. Tutorial #4 data representation
96. 96. XI. Tutorial #5 Linear regression
97. 97. XI. Tutorial #5 Linear regression
98. 98. XI. Tutorial #5 Linear regression
99. 99. XI. Tutorial #5 Linear regression
100. 100. XII. Tutorial #6 MNIST: Image recognition in Google Map * Source: Oriol Vinyals – Research Scientist at Google Brain
101. 101. XII. Tutorial #6 MNIST
102. 102. XII. Tutorial #6 MNIST: data set
103. 103. XII. Tutorial #6 MNIST
104. 104. XII. Tutorial #6 MNIST
105. 105. XII. Tutorial #6 MNIST
106. 106. XII. Tutorial #6 MNIST
107. 107. XII. Tutorial #6 MNIST
108. 108. XII. Tutorial #6 MNIST
109. 109. XII. Tutorial #6 MNIST
110. 110. Challenges Computing
111. 111. Thanks you! Q&A