Photo by Liam Charmer on Unsplash

Having read the Network in Network (NiN) paper by Lin et al a while ago, I came across a peculiar convolution operation that allowed cascading across channel parameters, enabling learning of complex interactions by pooling cross-channel information. …

The high computational cost of inferring from a complicated and often intractable, ‘true posterior distribution’ has always been a stumbling block in the Bayesian framework. However (and thankfully), there are certain inference techniques that are able to achieve a reasonable approximation of this intractable posterior with something… tractable.

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This year celebrates the 50th anniversary of the paper by Rudolf E. Kálmán that conferred upon the world, the remarkable idea of a Kalman Filter. Appreciation for the beauty (and simplicity) of this filtering technique often gets lost in technical, verbose definitions like the one found on Wikipedia:

In statistics…

Picture by Franki Chamaki on Unsplash

If a picture says a thousand words, your data probably says a lot more! You just need to listen.

A few months ago, working on a coursework assignment, we were posed with a problem: to develop an analytical model capable of detecting malicious or fraudulent transactions from credit card data-logs

Bayesian Inference is based on generative thinking

In the Machine Learning domain, we often find ourselves in the pursuit of inferring a functional relationship between the attribute variable(s) (i.e. features or simply, input data: x) and its associated response (i.e. the target variable: y). Being able to learn this relationship allows one to build a model that…

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Let's start with a small exercise. But first I have to ask you to put on your fitness tracker; You do own one right? Oh.. you thought the exercise was mental? Disastrous. The point is, if you’ve been even a tiny bit observant with the fitness device that sits on…

Photo by Volkan Olmez on Unsplash

When we think of an image, it's almost natural to think of a two-dimensional (2-D) representation right? Well, in reading the previous sentence carefully, ‘almost’ is the keyword.

In a recent paper, Scaling down deep learning, Sam Greydanus introduced a rather cool 1-D image dataset called the MNIST 1-D labelling…

Photo by Matthew Osborn on Unsplash

Back in high-school, we’ve all had that phase of derision in response to frequent reprimands issued by our teachers to scrupulously demonstrate the steps leading to a solution, especially while working out questions in an examination. “It promotes readability and calls attention to your intuition!”, they said. I vividly remember…

Anwesh Marwade

A final year student pursuing Masters in Data Science and Computer Vision. A motivated learner with a liking towards Sports Analytics.

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