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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.
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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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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 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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Can anyone articulate what the key differences are between SAS and SQL? I haven't worked much with SAS but went on a weeks training course, and basically it seemed like the equivalent but more convoluted and was able to do graphs.
Would appreciate some key bullet differences between them.
Standard SQL is a language to query, manipulate and define data in any(!) database. It is like the "latin language" of DB systems. Everyone knows it in order to perform standard tasks. SAS is like an extension to that with many functions.
I found a good document:
http://www.sascommunity.org/mwiki/images/5/52/CMSSUG-0506-SQL.pdf
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What is the measurement of the application's reliability as a non-functional requirement?
You cannot measure reliability as it is linked to a bunch of different factors. Uptime can be one of them, for web apps. For other software, it can be something like the prevention of data loss under any circumstances.
You'll have to define it more exactly by breaking it down into your detailed expectations, then you can quantify values to measure against.
It is a core problem with nonfunctional requirements that you cannot measure them easily. You'll have to work around that as described above.