Join the Shiny Community every month at Shiny Gatherings
Appsilon created a dashboard in collaboration the International Institute for Applied Systems Analysis to deliver information about COVID-19 to policymakers. We implemented the solution with Python and Svelte.
In this article, we focus on the technical aspects of the machine learning solution we implemented for the xView2 competition. We used PyTorch to build our models for satellite image analysis and fast.ai to develop their critical parts.
As part of our D4G Initiative and with the support of a Google grant, Appsilon Data Science will be contributing to to the work of biodiversity conservationists at the National Parks Agency in Gabon in collaboration with the University of Stirling.
We recently took part in Hakuna-ma Data, a competition organised by DrivenData in partnership with Microsoft’s AI for Earth, which asked participants to build an algorithm for wildlife detection that would generalise well across time and locations...
Climate change will exacerbate the consequences of natural disasters. Developing countries will be hit the hardest. We helped Dr. Junko Mochizuki build a decision support tool for policymakers in Madagascar to better mitigate this risk.
Data scientists must check if the story behind the data makes sense. This requires business knowledge about the data. It's our responsibility to be curious, to explore and challenge to avoid logical problems in the data to save clients money.
Our CTO used transfer learning to build an object detection model on a small dataset - the model can recognize certain animals in images.
Many real estate developers use online systems for sales. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.
