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Introduce Deep learning & A.I. Applications

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Understand Artificial Intelligence (Deep learning) & A.I. Applications
인공지능(딥러닝)의 개요와 인공지능 응용 사례

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Introduce Deep learning & A.I. Applications

  1. 1. 인공지능(딥러닝) 이해와 응용 Deep Machine Learning & Applications KOSS Lab. 2기 Mario Cho (조만석) hephaex@gmail.com
  2. 2. Contents • 기계학습 (NNs) • Neural Networks • 딥러닝 기술 • 활용방안
  3. 3. 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 Open Source Software Developer ◆ OPNFV (NFV&SDN) & OpenStack ◆ Machine Learning (TensorFlow) Book ◆ Unix V6 Kernel Korea Open Source Software Lab. Mario Cho hephaex@gmail.com
  4. 4. Today’s information * http://www.cray.com/Assets/Images/urika/edge/analytics-infographic.html
  5. 5. The Future of Jobs “The Fourth Industrial Revolution, which includes developments in previously disjointed fields such as artificial intelligence & machine-learning, robotics, nanotechnology, 3-D printing, and genetics & biotechnology, will cause widespread disruption not only to business models but also to labor market over the next five years, with enormous change predicted in the skill sets needed to thrive in the new landscape.”
  6. 6. 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.
  7. 7. Required of New type of Computing understand information, to learn, to reason, and act upon it
  8. 8. Traditional learning vs Deep Machine Learning Eiffel Tower Eiffel Tower RAW data RAW data Deep Learning Network Feature Extraction Vectored Classification Traditional Learning Deep Learning
  9. 9. Neuron
  10. 10. Human Intelligence
  11. 11. Brain Map
  12. 12. hippocampus
  13. 13. Neural network vs Learning network Neural Network Deep Learning Network
  14. 14. Neural Network W1 W2 W3 f(x) 1.4 -2.5 -0.06
  15. 15. Neural Network 2.7 -8.6 0.002 f(x) 1.4 -2.5 -0.06 x = -0.06×2.7 + 2.5×8.6 + 1.4×0.002 = 21.34
  16. 16. Neural Network : x1 XNOR x2 +1 x1 x2 +1 a1(2) a2(2) a1(3) -30 20 20 10 -20 -10 -10 10 -20 x1 x2 a1(2) a2(2) a1(3) 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 Output 10
  17. 17. Multi-layer Neural Networks Train this layer first
  18. 18. Multi-layer Neural Networks Train this layer first then this layer then this layer then this layer finally this layer
  19. 19. Tic Tac Toc
  20. 20. AlphaGo RAW data Layer1 Policy network & value network Selection Expansions Evaluation Backup ~ Layer13
  21. 21. Deep learning - CNN
  22. 22. Image recognition in Google Map * Source: Oriol Vinyals – Research Scientistat Google Brain
  23. 23. Deep Learning Hello World == MNIST
  24. 24. MNIST (predict number of image)
  25. 25. CNN (convolution neural network) training
  26. 26. MNIST
  27. 27. Old Character Recognition
  28. 28. Human-Level Object Recognition • ImageNet • Large-Scale Visual Recognition Challenge –Image Classification / Localization –1.2M labeled images, 1000 classes –Convolutional Neural Networks (CNNs) has been dominating the contest since.. – 2012 non-CNN: 26.2% (top-5 error) – 2012: (Hinton, AlexNet)15.3% – 2013: (Clarifai) 11.2% – 2014: (Google, GoogLeNet) 6.7% – 2015: (Google) 4.9% – Beyond human-level performance
  29. 29. Hierarchical Representation of Deep Learning * Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”.
  30. 30. * Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks”. Deep Learning (object parts)
  31. 31. Face extraction method
  32. 32. Face recognition data- sets?
  33. 33. Human-Level Face Recognition • Convolutional neural networks based face recognition system is dominant • 99.15% face verification accuracy on LFW dataset in DeepID2 (2014) – Beyond human-level recognition Source: Taigman et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR’14
  34. 34. Image Recognition * Source: Oriol Vinyals – Research Scientistat Google Brain
  35. 35. Object Classification and Detection
  36. 36. How to the Object recognition ?
  37. 37. Image Caption Generation
  38. 38. Language Generating * Source: Oriol Vinyals – Research Scientistat Google Brain
  39. 39. Scene Parsing [Farabet et al. ICML 2012, PAMI 2013]
  40. 40. Scene Parsing [Farabet et al. ICML 2012, PAMI 2013]
  41. 41. Auto pilot car
  42. 42. Automatic Colorization of Black and White Images
  43. 43. Generate sounds on old-movies
  44. 44. Automatic Machine Translation • Automatic Translation of Text. • Automatic Translation of Images.
  45. 45. Automatic Handwriting Generation
  46. 46. Text Generation
  47. 47. create stylized images from rough sketches.
  48. 48. Inspirer Humanity
  49. 49. Thanks you! Q&A

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