Row 4760

Row ID: 4760 | Dataset Entry | Axioma AXP Content Repository

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TL;DR: Tesla uses lightweight trigger classifiers to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes. How Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A 5-minute visual guide: [How Tesla sets up their iterative ML pipeline](https://open.substack.com/pub/codecompass00/p/tesla-data-engine-trigger-classifiers?r=rcorn&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true) P.S.: I spent several hours researching and preparing a visual deep dive of Tesla’s data engine as pioneered by Andrej Karpathy. The post lays out the iterative recipe of how Tesla improves it's fully self-driving and Autopilot capabilities.

https://preview.redd.it/tt1m3sevgtvc1.jpg?width=1456&format=pjpg&auto=webp&s=1d5d367d0943c31af6b49a94699123f6a73d2f03

https://preview.redd.it/8ffs2uevgtvc1.jpg?width=1456&format=pjpg&auto=webp&s=a88d8547ee3303de05339bcb01547ca893e1e1f6

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FieldValue
text TL;DR: Tesla uses lightweight trigger classifiers to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes. How Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A 5-minute visual guide: [How Tesla sets up their iterative ML pipeline](https://open.substack.com/pub/codecompass00/p/tesla-data-engine-trigger-classifiers?r=r…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-04-21
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url_encoded Z0FBQUFBQm5Lak9GVlR5MDZNZ1lrbXNKWEY4RU1FZTU3V1lIWU84aUg4NEFmVzVBSl9TYnFJM2wxOEJhVXg0WHN6SHl6WVcyMG13cUkyUU5pdGctbHRIeklmYVFkZEcwSHBTbkFGaDZiSC04SFhjZk04M3pCNl94dm90MWdOWGEyLTdoeVdQU0JhVVZFZ19HTUdSS2JmYVczVHA1dVhUR2V5ZHEtVDhRQWw1VEh4dFMxT1dvZjZMdFJxb0NWSUhsc2hWd2xiTS1fMlh3N1ZjNWJYR0VJYWRVbkV1NFVkTHBQUT09

Raw Record

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  "text": "TL;DR: Tesla uses lightweight trigger classifiers to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes.  \nHow Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A 5-minute visual guide: [How Tesla sets up their iterative ML pipeline](https://open.substack.com/pub/codecompass00/p/tesla-data-engine-trigger-classifiers?r=rcorn&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true)  \nP.S.: I spent several hours researching and preparing a visual deep dive of Tesla’s data engine as pioneered by Andrej Karpathy. The post lays out the iterative recipe of how Tesla improves it's fully self-driving and Autopilot capabilities.  \n\n\nhttps://preview.redd.it/tt1m3sevgtvc1.jpg?width=1456&format=pjpg&auto=webp&s=1d5d367d0943c31af6b49a94699123f6a73d2f03\n\nhttps://preview.redd.it/8ffs2uevgtvc1.jpg?width=1456&format=pjpg&auto=webp&s=a88d8547ee3303de05339bcb01547ca893e1e1f6\n\n​",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-04-21",
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