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
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Artificial Neural Network Seminar - Google Brain
- 1. GOOGLE BRAIN Rawan Al-Omari and Zeina Al-Helwani Damas ITE, AI Dept. -2012 ANN Presentation – In Supervision of Dr. Maisa Abo AlKassem
- 10. Infant’sVision
- 11. Google X Lab
- 12. Google Research Team Stanford Andrew Y. Ng and Jeff Dean
- 14. Machine Learning - AndrewY. Ng *Machine Learning; a branch of artificial intelligence research concerned with developing learning algorithms.
- 15. Open Questions!
- 16. Open Questions! Can We simulate these neurons?
- 17. Open Questions! Can We simulate these neurons? If we think of our neural network as simulating a very small-scale “newborn brain”
- 18. 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?
- 19. 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!
- 20. Previous Work
- 21. Supervised Learning • It uses Labeled Data! Labeled Data Learning Process
- 27. The Research
- 28. Why Unlabeled Data?! • Cost
- 29. Why Unlabeled Data?! • Cost • Available Data
- 30. Why Unlabeled Data?! • Cost • Available Data • Malicious Data
- 31. Why Unlabeled Data? - Malicious Data
- 32. Why Unlabeled Data? - Malicious Data Guerrilla
- 33. Why Unlabeled Data? - Malicious Data Guerrilla
- 34. Why Unlabeled Data? - Malicious Data Guerrilla Kitkat
- 36. Data Set & Test Set
- 37. YOUTUBE10,000,000 images 16,000 CPU Cores 1 Billion Connection
- 40. Training Duration OVER THREE DAYS !!
- 41. Image Features
- 47. Model • Autoenocoders • Pooling • Local Contrast
- 48. Local Contrast
- 52. Architecture
- 53. Architecture
- 54. Architecture
- 55. Architecture
- 57. Architecture Layer1 Image size 200 first sub layer
- 58. Architecture Layer1 Image size 200 second sub layer
- 59. Architecture Layer1 Image size 200 third sub layer
- 60. Architecture Layer1 Image size 200 fourth sub layer
- 61. Layer1Layer9…..
- 62. Cats and Faces Detector
- 66. LargeScale Largest network to date
- 69. Experiments
- 70. Experiments
- 71. Experiments Google Brain • 74.8% cat • 76.7% human body
- 72. Experiments Google Brain • 74.8% cat • 76.7% human body Best linear filters • 67.2% cat • 68.1% human body
- 73. 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
- 76. 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
- 77. 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
- 78. Conc l us i on
- 79. 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!