Here's a quick question for you. If you "and" two values in Python, what is the type of the return? A boolean, right? Nope. Read More

Every now and then, I’m working on a project, when I’m amazed by the sheer power of reusable code. This happened with my last project, where I became interested in how the Arab media reports news compared to its American counterparts. Read More

Deep learning-based comuter vision models like Inception Net have achieved state-of-the-art performance on image recognition. However, that doesn't mean that they don't have blindspots and biases. Here's a few of them, along with interactive aplications for you to try it out yourself. Read More

Deep learning remains somewhat of a mysterious art even for frequent practitioners, because we usually run complex experiments on large datasets, which obscures basic relationships between dataset, hyperparameters, and performance. The goal of this notebook is to provide some basic intuition of deep neural networks by running very simple experiments on small datasets that help understand trends that occur generally... Read More

We all know that deep neural networks (DNNs) are great for image recognition and speech processing. What about good ol' numerical datasets? I compared DNNs to other standard ML algorithms on many public classification datasets from the UCI ML repository, and here are the results. Read More

Today, I spent some understanding the Expectation-Maximization (EM) algorithm. It was the 8th or 9th time I had tried to understand it, because even though there are many nice tutorials about it online, about three quarters of the way through them, my eyes start to glaze over the math, and I end up leaving with only a “high-level understanding” of... Read More

Is machine learning research making the world a better place? I think about this sometimes when I attend conferences and see poster after poster proving theoretical guarantees for machine learning algorithms that already work well, or algorithm after algorithm that claims to achieve records results on standard image recognition datasets. Read More

Many of us, consciously or subconsciously, treat our research as fragile, when in reality, we should be stress-testing it. Here, I share 3 ways to make the methods we develop more robust. Read More

Removing noise from data is an important first step in machine learning. What can data scientists learn from noise-canceling headphones? Read More

A basic concept in Quranic studies is the difference between its meccan surahs/chapters (revealed before the Prophet Muhammad migrated from trading city of Mecca to oasis town of Medina) and its medinan chapters (revealed after the migration). Read More

Over the last few years, researchers have applied deep learning to all sorts of datasets in biology and medicine. On almost every problem – from identifying protein-DNA interactions to diagnosing Alzheimer’s disease from brain scans – deep learning techniques have performed remarkably well, leaving traditional machine learning in the dust. Read More

I was recently selected to be one of the 30 students that received the Paul and Daisy Soros Fellowship for graduate students. Going in, the interview for the fellowship was quite the black box for me – I had no idea what questions to expect, so I spent about 12 hours over the span of two weeks, practicing all sorts... Read More