Mahdi Dibaiee
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e3e3fecf4d
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refactor: working version with command-line utilities
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2019-03-31 09:52:00 +04:30 |
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Mahdi Dibaiee
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fe3f539d7d
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updates
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2019-03-07 06:55:23 +03:30 |
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Mahdi Dibaiee
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8477c02aae
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fix: use correct order for prediction
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2019-03-05 15:23:29 +03:30 |
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Mahdi Dibaiee
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3dcafddb8c
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refactor(data): include latitude longitude in columns, not indices
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2019-03-05 11:29:30 +03:30 |
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Mahdi Dibaiee
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865cc775ed
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fix(nn): better normalization, weight initialization and activation
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2019-02-28 17:22:50 +03:30 |
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Mahdi Dibaiee
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c28fc0850f
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updates
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2019-02-28 13:34:47 +03:30 |
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Mahdi Dibaiee
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f268e72244
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feat(temps): various temperatures
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2019-02-27 15:06:20 +03:30 |
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Mahdi Dibaiee
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d8365d6285
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feat(models): train models and evaluate them
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2019-02-26 11:50:31 +03:30 |
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Mahdi Dibaiee
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0d9a0068b1
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feat(nn): first model for predicting temp and precip
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2019-02-20 09:06:03 +03:30 |
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Mahdi Dibaiee
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c490f2006b
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feat(draw): draw dataframe on map
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2019-02-17 09:50:20 +03:30 |
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Mahdi Dibaiee
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a2ff08b195
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fix(data.py): precipication value was same as temp
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2019-02-14 12:36:09 +03:30 |
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Mahdi Dibaiee
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4318cf71be
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feat(tf): transform dataframe to tensorflow dataset
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2019-02-12 08:41:33 +03:30 |
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Mahdi Dibaiee
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ef604661ca
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feat(data): seasonal temp/precip data + distance to water
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2019-02-11 14:49:14 +03:30 |
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Mahdi Dibaiee
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caa1b0443c
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fix(data.py): optimize for optimal performance and generate data
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2019-02-08 18:14:57 +03:30 |
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Mahdi Dibaiee
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902be97332
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feat(data.py): data-reading file
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2019-02-03 09:04:28 +03:30 |
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Mahdi Dibaiee
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37d26dba75
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initial version
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2019-02-02 16:16:38 +03:30 |
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