Category: Blog

When you Google the terms “Deep learning and Medicine” or “Machine learning and medicine”, most of the articles you see will probably be about its uses in radiology-based diagnosis, electronic healthcare records, basically the clinical data. Over the past couple of years, there has been increasing ML research on the more cellular level, focusing on … Continue reading Zooming into the cells – Genomics and Bioinformatics

Generative modeling and GANs are popular in the media today because of their success in various tasks like generating deep fakes, digitally painting realistic portraits, converting images from one style to another, denoising images, etc. While these are really cool innovations, I think the translation of these ideas into the industry and our daily lives … Continue reading 3 Ways GANs can Change Medicine

Previously, I discussed in-depth the vanishing gradient problem in GANs and how it leads to unstable and ineffective learning. I then introduced the Wasserstein GAN, one of the popular solutions to this problem. While most research efforts into GANs have been in line with this goal of improving training stability, the game doesn’t end here. There … Continue reading Improving the GAN Part 2: Conditional and Controllable generation

In my previous post, I gave an introduction to generative adversarial networks (GANs), discussed the basic training algorithm, and implemented a basic GAN for generating handwritten digits. While this basic model works well and has been shown to produce decent images, there are many difficulties that one can encounter during training, a popular one being … Continue reading Improving the GAN

You must be wondering who these random people in the above picture are. What if I told you they don’t exist and were created by a computer? I was pretty amazed when I saw this at an AI talk I attended a couple of years back, and it still amazes me to this day. These … Continue reading Introduction to Generative Adversarial Networks

I’m sure all of us, when learning something new, have had moments of inspiration where we’d think, “Oh wow! This idea makes sense and is so brilliant.” It’s these moments that I always look forward to, and what attracted me to machine learning was that I had these moments almost every day, only motivating me … Continue reading Bayes Theorem – A primer

Last June, I was overjoyed when I got accepted into the MSc Machine Learning program at UCL. Pursuing this degree was my aspiration every since the second year of my undergraduate studies. Although I must confess, making the shift from a pure life science subject to a quantitative one, I was quite nervous and didn’t … Continue reading Three Lessons I learnt during my Machine Learning degree

“Deep Learning” has become the buzzword of the 21st century! It’s used in so many applications and devices, like the iPhone where it does facial recognition to log in, Facebook where it helps in automatic tagging or Snapchat where it reads our face to apply funny filters. Extensive deep learning research is also happening in … Continue reading Deep Learning Demystified

“The 21st century is a digital book. … Your bank records, medical histories, voting patterns, emails, phone calls, your damn SAT scores! [The] algorithm evaluates people’s past to predict their future. …” Above is a dialogue from one of my favorite Marvel movies, Captain America: The Winter Soldier, and I think it truly encompasses the … Continue reading What is machine learning?