BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Pacific Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20201022T230802Z DESCRIPTION:In episode one of the Grandmaster Series\, you’ll learn from three members of the Kaggle Grandmasters of NVIDIA (KGMON) team Chris Deot te\, Bo Liu\, and Gilberto Titericz. Watch this video to learn how they bu ilt the winning ML model for the SIIM-ISIC Melanoma Classification Kaggle competition. \n \nIn this competition\, the team had to create ML models t o identify skin lesions from patients’ images and determine which images are most likely to represent a melanoma. The winning ML model was able to identify melanoma earlier and more accurately than the average dermatolog ist. \n \nSubscribe to our YouTube channel to see a new Grandmaster Series episode each month. \n \nWatch the webinar here on Tuesday\, October 27 a t 8 am PT. https://youtu.be/L1QKTPb6V_I\n DTEND;TZID="Pacific Standard Time":20201027T090000 DTSTAMP:20201022T230802Z DTSTART;TZID="Pacific Standard Time":20201027T080000 LAST-MODIFIED:20201022T230802Z LOCATION:https://youtu.be/L1QKTPb6V_I PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:Grandmaster Series – How to Build a World-Class ML Model for Melanoma Detection TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E008000000000088B6268DA8D601000000000000000 010000000F9ABCE8127DBC349AE46013A4BBACD45 X-ALT-DESC;FMTTYPE=text/html:

In epi sode one of the Grandmaster Series\, you’\;ll learn from three member s of the Kaggle Grandmasters of NVIDIA (KGMON) team Chris Deotte\, Bo Liu\, and Gilberto Titericz. Watch this video to learn how they built the winning ML model for the S IIM-ISIC Melanoma Classification Kaggle competition. \;

 \;

In this competition\, the team had to c reate ML models to identify skin lesions from patients’\; images and determine which images are most likely to represent a melanoma. The winnin g ML model was able to identify melanoma earlier and more accurately than the average dermatologist. \;

 \;

Subscribe to our YouTube channel to see a new Grandmaster Series episode each month. \;

 \;

Watch the webinar here on Tuesday\, October 27 at 8 am PT. https://youtu.be/L1QKTPb6V_I

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