4c2bfb1af9
Jupyter notebook used for 1) training the NN, 2) evaluating its performance on the NEOPOP test set and on the real NEOs dataset
main
Vanessa Vichi2024-05-12 10:24:46 +02:00
9d4c656139
DataFrames with the attributable elements
Vanessa Vichi2024-05-12 10:22:38 +02:00
fb3ba5006d
Saved weights of Models 1,2 after a 500-epoch training (for both models) and a 1000-epoch training for Model 1
Vanessa Vichi2024-05-12 10:19:36 +02:00
2077014614
Conversion from Keplerian elements to attributable elements
Vanessa Vichi2024-05-11 10:58:40 +02:00
66775f772a
Jupyter Notebook for pre-processing of the NEOs DataFrame
Vanessa Vichi2024-05-11 10:49:36 +02:00
dc610efe37
Jupyter notebook for choosing the best initialization technique
Vanessa Vichi2024-05-09 10:04:46 +02:00
b3196ff82b
Jupyter notebook for evaluating the baseline performance: comparison of various metrics for the baseline model, the linear regression model and the polynomial regression model of degrees 2 and 3
Vanessa Vichi2024-05-09 09:57:48 +02:00
fea26621fc
Jupyter notebook for splitting the NEOPOP dataset into training, validation and test (with checks over the distribution of the various parts)
Vanessa Vichi2024-05-09 09:54:30 +02:00
67bbd480a4
Jupyter notebook for preliminary data exploration
Vanessa Vichi2024-05-09 09:50:42 +02:00
66021848e3
NEOPOP DataFrame split into training, validation, test
Vanessa Vichi2024-05-09 09:47:58 +02:00