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Articles by Anupama Rajkumar
Platform-Mesh, Hub and Spoke, and Centralised | 3 Types of data team
In the “ever rapidly changing landscape of Data and AI” (!), understanding data and AI architecture has never been more critical. However something many leaders overlook is the importance of data team structure. While many of you reading this probably identify as the data team, something most don’t realise is how limiting that mindset can be.
Data vs. Business Strategy
There seems to be a consensus that leveraging data, analytics, and AI to create a data-driven organization requires a clear strategic approach. However, there is less clarity and agreement on exactly what this strategic approach should look like in practice. This article provides a short overview of what strategy work I believe is required to become data-driven. It offers a summary of a detailed deep-dive I recently wrote and is the fourth installment in a series demystifying data strategy.
Introducing a Novel Very High-Resolution Dataset of Landfills and Waste Dumps
Notes from Industry A novel dataset consisting of very high resolution multi-spectral satellite images of landfills from Germany, Hungary, Serbia, India and Brazil Photo by Pop & Zebra on Unsplash Waste disposal is a serious problem with most of the world’s waste finding it’s way into the dumps in south-east Asian and African countries. In addition to landfills, waste is illegally disposed off along river banks and beaches.
Gotta Autoencode ’em All
Today, let’s talk about Autoencoder. To do that, I will use an analogy with Pokémon, OK? This will be fun. Imagine that you are Ash Ketchum (a.k.a Satoshi) and a big Snorlax appears in the middle of your path, preventing you from following your journey to be a Pokémon master. Snorlax — Author: Lazarus In this case, let’s assume that you just need to throw the Poké Ball in the Snorlax to capture it and continue your journey. We can say that the Poké Ball is a Perfect Autoencoder.
Autoencoders & their Application in Remote Sensing
An autoencoder is a neural network that consists of an input layer, one or more hidden layer(s) and an output layer, pretty much like any other neural network that you may have heard of or worked with. But there are some very specific characteristics that an autoencoder possesses which makes it unique and they are: The input and output layers of an autoencoder always have the same dimension.
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