You can split each polygon into a collection of triangles and sum up the areas. Not really sure why you would bother with ML.
Anyway if you approximates these polygons as images you could maybe train a CNN. Look at the image classification networks which provide bounding boxes.
[Disclaimer: I work for a company that provides a platform for developing conversational AI systems]
The platform used by the company I work for has a sentiment analysis component, so you can recognise if the user input expresses certain emotions. The dialogues are encoded in 'flows', which are graphs with an initial trigger consisting of output nodes and ...
Tomas Mikolov's mention of gradient clipping in a single paragraph of his PhD thesis in 2012 is the first appearance in the literature.
The first source (Mikolov, 2012) in the Deep Learning book is Mikolov's PhD thesis and can be found here. The end of section 3.2.2 is where gradient clipping is discussed, only it's called ...
There are quite a few ROS (Robot Operating System) based humanoid robots. Not all of them will be open-source down to the firmware level but most will at least be to the control level. I'm not aware of any that have open-source hardware as well.
Some examples include:
I have not found any simple implementation of a naive EBMT system, but I found some articles, papers and books that may be helpful (although I haven't read them, apart from the first and last one), so I will list them below.
The web article Example-based machine translation provides a decent high-level explanation of example-based machine translation.
Yes, there are many, actually. A Google search turned this paper Artificial Neural Networks in Medical Diagnosis (2011) by Al-Shayea up.
Not only are they used in disease diagnosis, but even with things like prescribing medicines. In fact, the top project for a hackathon at my school analysed thousands of research articles, and took a patient's medication ...