Statistical Learning as the Ultimate Agile Development Tool
author:
Peter Norvig,
Google
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| Slides | |
| 0:00 | Conference on Information and Knowledge Management |
| 0:08 | Statistical learning from data as the ultimate agile development tool |
| 0:40 | Agile Development Principles |
| 1:35 | Keep it Simple (1) |
| 3:04 | Keep it Simple (2) |
| 3:18 | Keep it Simple (3) |
| 3:24 | Keep it Simple (4) |
| 3:56 | Rational Programming |
| 4:55 | Text Data |
| 5:05 | Segmentation (1) |
| 5:39 | Segmentation (2) |
| 6:03 | Segmentation (3) |
| 6:07 | Segmentation (4) |
| 6:22 | Segmentation (5) |
| 6:26 | Segmentation (6) |
| 6:38 | Segmentation (7) |
| 7:05 | Segmentation (8) |
| 7:26 | Segmentation (9) |
| 8:10 | Trained on 1.7B words English (1) |
| 8:23 | Trained on 1.7B words English (2) |
| 8:29 | Trained on 1.7B words English (3) |
| 8:34 | Trained on 1.7B words English (4) |
| 8:44 | Trained on 1.7B words English (5) |
| 8:47 | Trained on 1.7B words English (6) |
| 8:53 | Trained on 1.7B words English (7) |
| 9:03 | Trained on 1.7B words English (7) |
| 9:10 | Trained on 1.7B words English (8) |
| 9:39 | Mistakes were made (1) |
| 10:18 | Mistakes were made (2) |
| 10:42 | Mistakes were made (3) |
| 10:52 | Mistakes were made (4) |
| 11:03 | Mistakes were made (5) |
| 11:32 | Mistakes were made (6) |
| 11:46 | Spelling (1) |
| 12:10 | Spelling (2) |
| 12:28 | Example |
| 13:39 | Spelling with Statistical Learning (1) |
| 13:41 | Spelling with Statistical Learning (2) |
| 13:53 | Spelling with Statistical Learning (3) |
| 13:56 | Spelling with Statistical Learning (4) |
| 14:00 | Spelling with Statistical Learning (5) |
| 14:20 | Example - Code (1) |
| 14:48 | Google Sets (1) |
| 15:09 | Google Sets (2) |
| 15:33 | Google Sets (3) |
| 16:02 | Google Sets (4) |
| 16:09 | Google Sets (5) |
| 16:43 | Google Sets (6) |
| 16:59 | Google Sets (7) |
| 17:15 | Google Sets (8) |
| 17:40 | Google Sets (9) |
| 17:50 | Google Sets (10) |
| 18:41 | Statistical Machine Translation (1) |
| 19:24 | Statistical Machine Translation (2) |
| 19:38 | Statistical Machine Translation (3) |
| 19:44 | Statistical Machine Translation (4) |
| 19:47 | Statistical Machine Translation (5) |
| 20:13 | Statistical Machine Translation (6) |
| 20:18 | Statistical Machine Translation (7) |
| 20:22 | Statistical Machine Translation (8) |
| 20:41 | Statistical Machine Translation (9) |
| 20:44 | Statistical Machine Translation (10) |
| 20:57 | Example- Chinese (1) |
| 21:00 | Example- Chinese (2) |
| 21:17 | Example- Chinese (3) |
| 22:00 | Example- Chinese (4) |
| 22:21 | Example- Chinese (6) |
| 22:31 | Example- Chinese (7) |
| 22:35 | Example- Chinese (8) |
| 22:37 | Example- Chinese (9) |
| 22:38 | Example- Chinese (10) |
| 22:39 | Example- Chinese (11) |
| 22:39 | More Data Still Helps |
| 22:58 | Image Data |
| 23:03 | Finding Canonical Images |
| 24:39 | Compare low-level features |
| 25:06 | Induced Graph (1) |
| 25:44 | Induced Graph (2) |
| 26:36 | Induced Graph (3) |
| 26:42 | SIFT Features |
| 27:08 | Induced Graph (4) |
| 27:32 | Learning People Annotation (1) |
| 27:41 | Learning People Annotation (2) |
| 28:18 | Learning People Annotation (3) |
| 28:49 | In Conclusion |
| 28:54 | Code is a Liability |
| 29:50 | Questions |
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F***ing idiot cameraman/director thought we would want to look at Peter rather than what he was talking about!
The slides are here, the audio is here; no need for name calling.
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