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. They called it the “cross channel parametric pooling layer” (if I remember correctly), comparing it to an operation involving convolution with a 1x1 convolutional kernel.

Skimming over the details at the time (as I often do with such esoteric terminology), I never thought I would be writing about this operation, let alone providing my own thoughts on its workings. But as it goes…

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. *See what I did there?

One such approximate inference technique that has gained popularity in recent times is the **Variational Bayes (VB).** Having a relatively **low computational cost** and a **good empirical approximation** has propelled it to drive the intuition behind successful models like the **Variational Auto-encoders** and more. …

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 and control theory,

Kalman filtering, also known aslinear quadratic estimation(LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, producing estimates ofunknown variablesthat tend to be more accurate than those based on a single measurement alone. …

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*. Excited, we got to the drawing board to start building our classification model. What started as a seemingly ‘grades-in-the-bag’ assignment quickly turned into a ‘not-so-straightforward’ project. The complications lay in certain artefacts of the supplied data-set which if not anything, at least made us question our naivety!

The thing with…

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 can *predict* a response, given any set of such attribute variables (i.e. the test data).

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 your wrist at the moment, you will have noticed a shiny light at the back (usually green). Now if you’ve been curious or a bit more observant, you’ll know that this light is used as a medium (pun unintended) to extract the pulse signal from your wrist! And that’s where…

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 it as, *“A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks.” *The benchmark being referred to is the MNIST dataset which is a dataset of hand-written digits that is quite well known within the machine learning (ML) or more generally, the data science space. Unlike the **original** MNIST…

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 our Mathematics teacher bellowing at a classroom full of iffy students, re-iterating the importance of this so-called *‘step-wise’* approach to presenting our solutions. …

A final year student pursuing Masters in Data Science and Computer Vision. A motivated learner with a liking towards Sports Analytics. anweshcr7.github.io