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Should I normalize input data to normal distribution before fit it into RNN? If yes, why? At the moment almost all the columns are right shifted, so it's not a normal distribution at all.
You do not necessarily need to transform the inputs to a normal distribution, but you might want to preprocess them so that the majority of each of their values is between 0 and 1. Otherwise, when using sigmoid functions for internal nodes, you may cause saturation. If your inputs are each U(0, 1000), for example, then there's no need to transform to normal distributions, but rather to just scale by 0.001.
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I'm building my arima model and I have a quarterly data. The total dataset is 104 (1996-2021), now how many train data and test data should I use? Inorder to have an accurate forecast data. Thanks
For ARIMA models, the rule of thumb is that you should have at least 50 but preferably more than 100 observations.
You can split the whole datasets at a ratio of 80 to 20.
80% for training and 20% for testing.
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For the model in the pandas dataset, is it okay to use the target column when adding a new feature?
How does it affect the model ?
No, the general rule is you should not utilize the data which will not be available in testing or real-application. It will probably result in overfitting.
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Is there some sort of software that can do so? Specifically, I would like to visualize Resnet18. Is there no other way other than to just draw it myself? Here is an example of what I want to see:
Sample Architecture Visualization
You can use this one : http://alexlenail.me/NN-SVG/LeNet.html . It lets you visualize neural networks by letting you modify several parameters and finally lets you export the architectures as SVG files. You can also choose between 3 visualization styles, namely FCNN, LeNet & AlexNet.
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I'm looking for some guidance here. I primarily am a frontend developer. What I am trying to figure out is how an algorithm can be implemented to count kernels on an ear of corn.
From my initial research it seems there are a couple of different directions to go. Main ones I have seen are a SIRF type of implementation and others call for conversion to the HSV color space or LAB color space in order to then to normalizations and then counting.
For reference usually the corn that will be counted is "dent" corn. Here is an example:
This will be implemented in VB.net, but I can always translate the algorithm if needed.
Thank you for your help!
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I'd like to cluster/break down users into groups of 10-100 on a map with non-overlapping geo boundaries for each cluster.
What kind of database and query could I use to calculate that? Should I just use any database and use some sort of map reduce algorithm to calculate the clusters? Would something like a k-means clustering algorithm be useful here?
Can I do this with a query rather than a map reduce?
You can try a voronoi diagram. A voronoi diagram is the dual of a delaunay triangulation with some useful properties: https://alastaira.wordpress.com/2011/04/25/nearest-neighbours-voronoi-diagrams-and-finding-your-nearest-sql-server-usergroup.