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cool tut. nice to watch while working in a cluster, coding some python. i like that's its practical and providing an explanation about the math needed
Thank you for taking time to go through the tutorial and your positive comments!
have you guys written a paper based on your work (ipynb results)?
Not yet. Shoot an email if you are interested!
hello, can u help me pls, how to calculate seasonal water surface discharge of a river flow in my study area using Google Earth Engine ?
Hi I suggest asking in the GEE developers group for wider reach!
@@TheGeoICT where and how can i reach out to them pls? help
@@ShakoKuna You can reach out at biplov4geoict@gmail.com
Kindly help me with how to go about exporting the runoff image
Hi, you can use `plt.savefig(, bbox_inches="tight")` to export the plots.
is it ok to split test and train data randomly?
That's a great question! Since LSTM is a time-series algorithm, as long as your examples maintain the sequence in them, you could randomly split training and testing data at the example level. Happy coding!
cool tut. nice to watch while working in a cluster, coding some python. i like that's its practical and providing an explanation about the math needed
Thank you for taking time to go through the tutorial and your positive comments!
have you guys written a paper based on your work (ipynb results)?
Not yet. Shoot an email if you are interested!
hello, can u help me pls, how to calculate seasonal water surface discharge of a river flow in my study area using Google Earth Engine ?
Hi I suggest asking in the GEE developers group for wider reach!
@@TheGeoICT where and how can i reach out to them pls? help
@@ShakoKuna You can reach out at biplov4geoict@gmail.com
Kindly help me with how to go about exporting the runoff image
Hi, you can use `plt.savefig(, bbox_inches="tight")` to export the plots.
is it ok to split test and train data randomly?
That's a great question! Since LSTM is a time-series algorithm, as long as your examples maintain the sequence in them, you could randomly split training and testing data at the example level. Happy coding!