File:NASA ARSET- Training & Testing ML Models for Irregularly-Spaced Time Series of Imagery, Part 3-3 (vpHREZBTuF8).webm
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English: Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions
Part 3: Training & Testing ML Models for Irregularly-Spaced Time Series of Imagery Trainers: Sean McCartney Guest Instructors: John Just (Deere & Co.), Erik Sorensen (Deere & Co.) - Perform the process to set up and train a 1-D convolutional neural network (CNN) model that learns to detect crop-type from a satellite image - Follow steps to monitor model performance during training and how to choose appropriate hyperparameter adjustments - Plot predictions to validate performance after training You can access all training materials from this webinar series on the training webpage: https://go.nasa.gov/41QtlBu This training was created by NASA's Applied Remote Sensing Training Program (ARSET). ARSET is a part of NASA's Applied Science's Capacity Building Program. Learn more about ARSET: https://appliedsciences.nasa.gov/what-we-do/capacity-building/arset |
| Date | 20 March 2024, 16:12:59 (upload date) |
| Source | NASA ARSET: Training & Testing ML Models for Irregularly-Spaced Time Series of Imagery, Part 3/3 |
| Author | NASA |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
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| current | 19:13, 7 April 2024 | 1 h 29 min 25 s, 1,920 × 1,080 (272.58 MB) | OptimusPrimeBot (talk | contribs) | Imported media from https://www.youtube.com/watch?v=vpHREZBTuF8 |
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