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 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

Published in: Education, Technology

Artificial Neural Network Seminar - Google Brain

  1. 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. 2. Infant’sVision
  3. 3. Infant’sVision
  4. 4. Infant’sVision
  5. 5. Infant’sVision
  6. 6. Infant’sVision
  7. 7. Infant’sVision
  8. 8. Infant’sVision
  9. 9. Infant’sVision
  10. 10. Google X Lab
  11. 11. Google Research Team Stanford Andrew Y. Ng and Jeff Dean
  12. 12. Machine Learning - AndrewY. Ng *Machine Learning; a branch of artificial intelligence research concerned with developing learning algorithms.
  13. 13. Open Questions!
  14. 14. Open Questions! Can We simulate these neurons?
  15. 15. Open Questions! Can We simulate these neurons? If we think of our neural network as simulating a very small-scale “newborn brain”
  16. 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. 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. 18. Previous Work
  19. 19. Supervised Learning • It uses Labeled Data! Labeled Data Learning Process
  20. 20. LabeledVS Unlabeled
  21. 21. LabeledVS Unlabeled Cat
  22. 22. LabeledVS Unlabeled Cat
  23. 23. LabeledVS Unlabeled Cat Cat
  24. 24. LabeledVS Unlabeled Cat Cat
  25. 25. The Research
  26. 26. Why Unlabeled Data?! • Cost
  27. 27. Why Unlabeled Data?! • Cost • Available Data
  28. 28. Why Unlabeled Data?! • Cost • Available Data • Malicious Data
  29. 29. Why Unlabeled Data? - Malicious Data
  30. 30. Why Unlabeled Data? - Malicious Data Guerrilla
  31. 31. Why Unlabeled Data? - Malicious Data Guerrilla
  32. 32. Why Unlabeled Data? - Malicious Data Guerrilla Kitkat
  33. 33. Unsupervised Features Learning Self Taught Learning
  34. 34. Data Set & Test Set
  35. 35. YOUTUBE10,000,000 images 16,000 CPU Cores 1 Billion Connection
  36. 36. ImageNet22,000 Categories 16,000,000 images
  37. 37. Training Duration
  38. 38. Training Duration OVER THREE DAYS !!
  39. 39. Image Features
  40. 40. Features Pixels Edges
  41. 41. Face Parts (Combination of edges) Face Detectors
  42. 42. High-level Features
  43. 43. High-level Features
  44. 44. High-level Features
  45. 45. Model • Autoenocoders • Pooling • Local Contrast
  46. 46. Local Contrast
  47. 47. Local Contrast BEFOR
  48. 48. Local Contrast BEFOR
  49. 49. Local Contrast BEFOR AFTER
  50. 50. Architecture
  51. 51. Architecture
  52. 52. Architecture
  53. 53. Architecture
  54. 54. Architecture Layer1 Image size 200
  55. 55. Architecture Layer1 Image size 200 first sub layer
  56. 56. Architecture Layer1 Image size 200 second sub layer
  57. 57. Architecture Layer1 Image size 200 third sub layer
  58. 58. Architecture Layer1 Image size 200 fourth sub layer
  59. 59. Layer1Layer9…..
  60. 60. Cats and Faces Detector
  61. 61. Model Parallelism
  62. 62. Model Parallelism
  63. 63. Asynchronous Parallel Model
  64. 64. LargeScale Largest network to date
  65. 65. LargeScale HumanVisual Cortex 106
  66. 66. Experiments
  67. 67. Experiments
  68. 68. Experiments Google Brain • 74.8% cat • 76.7% human body
  69. 69. Experiments Google Brain • 74.8% cat • 76.7% human body Best linear filters • 67.2% cat • 68.1% human body
  70. 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. 71. 9.3% State-of-the-art
  72. 72. 9.3% State-of-the-art 15.8%Our method
  73. 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. 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. 75. Conc l us i on
  76. 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. 77. </end> Thank you!

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