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Artificial Neural Network Seminar - Google Brain
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Artificial Neural Network Seminar - Google Brain

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it's our seminar in artificial neural network course, at F.I.T.E, AI Dept. …

it's our seminar in artificial neural network course, at F.I.T.E, AI Dept.
it's about Google Brain project, and who they using neural network in building it .
actually it's a very interesting project they work on it .
for more information about this project :
http://nyti.ms/T5E71e

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  • 1. GOOGLE BRAIN Rawan Al-Omari and Zeina Al-Helwani Damas ITE, AI Dept. -2012 ANN Presentation – In Supervision of Dr. Maisa Abo AlKassem
  • 2. Infant’sVision
  • 3. Infant’sVision
  • 4. Infant’sVision
  • 5. Infant’sVision
  • 6. Infant’sVision
  • 7. Infant’sVision
  • 8. Infant’sVision
  • 9. Infant’sVision
  • 10. Google X Lab
  • 11. Google Research Team Stanford Andrew Y. Ng and Jeff Dean
  • 12. Machine Learning - AndrewY. Ng *Machine Learning; a branch of artificial intelligence research concerned with developing learning algorithms.
  • 13. Open Questions!
  • 14. Open Questions! Can We simulate these neurons?
  • 15. Open Questions! Can We simulate these neurons? If we think of our neural network as simulating a very small-scale “newborn brain”
  • 16. Can We simulate these neurons? Open Questions! If we think of our neural network as simulating a very small-scale “newborn brain” Show it YouTube video for a week, what will it learn?
  • 17. Can We simulate these neurons? Open Questions! If we think of our neural network as simulating a very small-scale “newborn brain” Show it YouTube video for a week, what will it learn? Google Brain LIKE Human Brain!
  • 18. Previous Work
  • 19. Supervised Learning • It uses Labeled Data! Labeled Data Learning Process
  • 20. LabeledVS Unlabeled
  • 21. LabeledVS Unlabeled Cat
  • 22. LabeledVS Unlabeled Cat
  • 23. LabeledVS Unlabeled Cat Cat
  • 24. LabeledVS Unlabeled Cat Cat
  • 25. The Research
  • 26. Why Unlabeled Data?! • Cost
  • 27. Why Unlabeled Data?! • Cost • Available Data
  • 28. Why Unlabeled Data?! • Cost • Available Data • Malicious Data
  • 29. Why Unlabeled Data? - Malicious Data
  • 30. Why Unlabeled Data? - Malicious Data Guerrilla
  • 31. Why Unlabeled Data? - Malicious Data Guerrilla
  • 32. Why Unlabeled Data? - Malicious Data Guerrilla Kitkat
  • 33. Unsupervised Features Learning Self Taught Learning
  • 34. Data Set & Test Set
  • 35. YOUTUBE10,000,000 images 16,000 CPU Cores 1 Billion Connection
  • 36. ImageNet22,000 Categories 16,000,000 images
  • 37. Training Duration
  • 38. Training Duration OVER THREE DAYS !!
  • 39. Image Features
  • 40. Features Pixels Edges
  • 41. Face Parts (Combination of edges) Face Detectors
  • 42. High-level Features
  • 43. High-level Features
  • 44. High-level Features
  • 45. Model • Autoenocoders • Pooling • Local Contrast
  • 46. Local Contrast
  • 47. Local Contrast BEFOR
  • 48. Local Contrast BEFOR
  • 49. Local Contrast BEFOR AFTER
  • 50. Architecture
  • 51. Architecture
  • 52. Architecture
  • 53. Architecture
  • 54. Architecture Layer1 Image size 200
  • 55. Architecture Layer1 Image size 200 first sub layer
  • 56. Architecture Layer1 Image size 200 second sub layer
  • 57. Architecture Layer1 Image size 200 third sub layer
  • 58. Architecture Layer1 Image size 200 fourth sub layer
  • 59. Layer1Layer9…..
  • 60. Cats and Faces Detector
  • 61. Model Parallelism
  • 62. Model Parallelism
  • 63. Asynchronous Parallel Model
  • 64. LargeScale Largest network to date
  • 65. LargeScale HumanVisual Cortex 106
  • 66. Experiments
  • 67. Experiments
  • 68. Experiments Google Brain • 74.8% cat • 76.7% human body
  • 69. Experiments Google Brain • 74.8% cat • 76.7% human body Best linear filters • 67.2% cat • 68.1% human body
  • 70. Experiments Google Brain • 74.8% cat • 76.7% human body Best linear filters • 67.2% cat • 68.1% human body OpenCV • 3% of 100,000 samples
  • 71. 9.3% State-of-the-art
  • 72. 9.3% State-of-the-art 15.8%Our method
  • 73. Dataset version 2009 (∼9M images, ∼10K categories) 2011 (∼14M images, ∼22K categories) State-of-the-art 16.7% 9.3% Our method 19.2% 15.8% 9.3% State-of-the-art 15.8%Our method
  • 74. Experiments - Stats 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% Random guess Best linear filter Google Brain Faces Human bodies Cats
  • 75. Conc l us i on
  • 76. Conclusion! • Largest network to date ! • Leading to significant advances in area as : – Machine Vision – Speech Recognition – Language Translation • Google Brain LIKE Human Brain.. it may just be a matter of Time!
  • 77. </end> Thank you!