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Deep Learning in the Real World

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The real opportunities and issues with actually using deep learning.

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Deep Learning in the Real World

  1. 1. Deep Learning in the Real World Lukas Biewald @L2K
  2. 2. CrowdFlower
  3. 3. O’Reilly
  4. 4. Excitement Around Deep Learning
  5. 5. Machine Learning vs Statistics Glossary (Robert Tibshirani) Machine Learning Statistics Learning Fitting Generalization Test Set Performance Supervised Learning Regression, Classification Unsupervised Learning Density estimation, clustering large grant = $1,000,000 large grant = $50,000 nice place to have a meeting: Snowbird, Utah, French Alps nice place to have a meeting: Las Vegas in August
  6. 6. Venture Capital Investment in Deep Learning https://medium.com/startup-grind
  7. 7. Inevitable Backlash
  8. 8. What’s Actually Working?
  9. 9. Image Recognition
  10. 10. Medical
  11. 11. What are the Challenges?
  12. 12. Proprietary and Confidential - Do Not Distribute Human Generated Code vs Machine Generated Code
  13. 13. 2001 Space Odyssey https://www.youtube.com/watch?v=MzIQUDQO-ag
  14. 14. Machine Learning Projects are Really Hard to Manage
  15. 15. Proprietary & Confidential34
  16. 16. Proprietary & Confidential35 Kaggle Accuracy 0% 18% 35% 53% 70% Baseline 12-May 13-May 14-May 15-May Accuracy Accuracy of Best Performing Model
  17. 17. Proprietary & Confidential36 Kaggle accuracy over time 0% 20% 40% 60% 80% 13-May 14-May 15-May 16-May 17-May 18-May 19-May 31-May 16-Jun 1-Jul 7-Jul Accuracy Accuracy of the Best Performing Model
  18. 18. Proprietary & Confidential37 Kaggle Participation 0 350 700 1050 1400 13-May14-May15-May16-May17-May18-May19-May31-May 16-Jun 1-Jul Number of Participating Teams
  19. 19. Proprietary & Confidential38 Netflix Prize
  20. 20. Self Driving Cars - Close or Far?
  21. 21. Machine Learning Can Be Unpredictable and Opaque
  22. 22. Image Classification Success
  23. 23. Image Classification Errors
  24. 24. Image Classification Errors
  25. 25. Image Classification Errors
  26. 26. Alpha Go’s Mistake
  27. 27. Criminal Risk Scores
  28. 28. Explainability of Neural Networks
  29. 29. Deep Learning Can Be Vulnerable to Hacking
  30. 30. https://github.com/jmgilmer/AdversarialMNIST
  31. 31. Glasses Fooling Face Recognition
  32. 32. Machine Learning Requires Training Data
  33. 33. The Effect of Better Algorithms
  34. 34. The Effect of Better Features
  35. 35. The Effect of More Data
  36. 36. The Effect of Cleaner Data
  37. 37. Where Do Data Scientists Spend Their Time?
  38. 38. Proprietary and Confidential - Do Not Distribute CrowdFlower AI Platform: Training Data • Multiple use cases • Multiple data formats • Templatized workflow Training Data Human-in- the-loop Machine Learning
  39. 39. Proprietary and Confidential - Do Not Distribute CrowdFlower AI Platform: Training Data • Image labeling for self driving cars • Pixel-level categorization done by machine and humans Training Data Human-in- the-loop Machine Learning
  40. 40. ???
  41. 41. The Combination of Humans and Computers is Powerful
  42. 42. Advanced Chess
  43. 43. AI Classifier Output Human in the Loop Confident
  44. 44. Confident Output Human Annotation AI Classifier Human in the Loop
  45. 45. Output Active Learning Human Annotation ConfidentAI Classifier Human in the Loop
  46. 46. United States Postal Service (1982)
  47. 47. Proprietary and Confidential - Do Not Distribute CrowdFlower AI Platform: Case Study Training Data Human-in-the- loop Machine Learning 400,000 structured support tickets create initial ML model 200,000 new support tickets per week fed into ML model 40% initial output by model; 60% handled by human review
  48. 48. Machine Learning Can Look at Far More Data than Humans
  49. 49. The Unreasonable Effectiveness of Data Revisited (Google Blog 2017)
  50. 50. Breakthroughs and Data Sets Alexander Wissner-Gross
  51. 51. Massive Free Datasets
  52. 52. Audio Set
  53. 53. Transfer Learning is the Future
  54. 54. New Data Sets
  55. 55. Freiburg Groceries Data Set
  56. 56. Inception
  57. 57. https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/object_loca
  58. 58. Visualizing Deep Learning Networks - Layer 1 https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
  59. 59. Visualizing Deep Learning Networks - Layer 2 https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
  60. 60. Visualizing Deep Learning Networks - Layer 3 https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
  61. 61. Visualizing Deep Learning Networks - Layer 4 https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
  62. 62. Visualizing Deep Learning Networks - Layer 5 https://blog.keras.io/how-convolutional-neural-networks-see-the-world.html
  63. 63. Using DNNs as feature extractors
  64. 64. Retraining Neural Networks (Fine Tuning)
  65. 65. Fine Tuning Accuracy Improvements 77% Accuracy With Fine Tuning 47% Accuracy Without Fine Tuning https://shuaiw.github.io/2017/03/09/smaller-faster-deep-learning-models.html
  66. 66. Dermatologist-level classification of skin cancer with deep neural networks
  67. 67. Multi Task Learning
  68. 68. https://sorenbouma.github.io/blog/oneshot/ One-Shot Learning
  69. 69. Synthetic Training Data
  70. 70. Thank You Lukas Biewald (@L2K)

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