Getting Started

Apply simple algorithms to images using map algebra

Null values will always be the ire of the data scientist and managing them correctly can be the difference between an application that breaks and an application that soars.

This is particularly important with GIS images, or any rasterized data. We cannot ignore nulls or fill them with zeros (removing is not an option!) because it would produce unsightly holes in the resulting image (see example below).

Images of GIS elevation data before and after spatial filter smoothing has been applied.
Images of GIS elevation data before and after spatial filter smoothing has been applied.
The digital elevation model or DEM on the left shows a raster dataset with null values; the image on the right shows how it would look after applying spatial filter smoothing to the null values. (image|bubjanes)

We must apply a technique called spatial filtering to fill nulls with information that reflects the context or surroundings in which we find them.

In order for the “AFTER” image (above) to appear…


Plug and play with transformers using Rasa and Huggingface

For over a year I have been trying to automate appointment scheduling using NLP and python, and I finally got it to work thanks to an amazing free open-source dialogue tool called Rasa.

The truth is a year ago, when I started this project, the tools I am using now were hardly available. Certainly not in the easy-to-use form that we find them today. As a testament to how quickly this science of artificial intelligence and NLP is moving, this time last year (June 2019) transformers like BERT had barely left the domain of academic research and were just beginning…

Brandon Janes

Freedom of information journalist-turned data scientist, interested in bringing data to life through machine learning and Python.

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