3 votes
Accepted

How do researchers actually code novel architectures and layers?

In this particular case, I don't know how are they implementing these complex layers, but in Keras/TensorFlow you can define your own layers by inheriting from ...
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  • 489
2 votes

Weather impact on plant growth

It's difficult to guess which option is going to give the best results, since it depends on many factors in the data. This is feature engineering, and while there are some general principles it's not ...
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  • 21.8k
2 votes

Approach for unsupervised time series clustering/segmentation

I would recommend a dimensional reduction algorithm such as t-SNE or UMAP, in order to visualize the different clusters in an unsupervised way. This is quite easy to do, as long as you normalize the ...
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2 votes
Accepted

Why can't I reproduce my results in keras using random seed?

Are you using a CPU or a GPU? If you are using a GPU, there is an additional source of randomness. To confirm this point, you can try to use TensorFlow with CPU only, or disable Cuda DNN but the model ...
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1 vote
Accepted

Optimize daily ice cream profit beased on simulation of all combinations input variables

I would propose a solution like this: Train a regression model which predicts the sales (target variable) based on all the features (both types: those you have control on and those you don't). ...
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  • 21.8k
1 vote

Find "seasonality" in a categorical time series in python

To do that you can use seasonal_decompose (https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/) .Before using it you will need to take care of Nan.After that you can use ...
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