NOT Just Tweaking Some Parameters

What data practitioners talk about when they talk about deep learning

In the world of data science and machine learning, people tend to talk about the shiny stories, publishable methods, state of the art experiments, the rainbows and butterflies. In reality, practitioners struggle with lots of challenges while learning and applying new methods, but their practical lived experiences are rarely shared for different reasons. I was recently following one of the few examples, in which someone shared their journey to the world of deep learning, without a lot of filtering. [Read More]

The Seeds of Bad Data Products!

The adoption of not-so-scientific facts and the path to harmful algorithms

How do harmful data products and algorithms get enforced on us and impact our lives? a question that is widely discussed nowadays, although I am not sure if “widely” is the right description of the current level of attention it takes. But there is another question that I find crucial which is, How do these products emerge in the first place and gain credibility?, How do crappy senseless products reach the market, proliferate our lives and put us in a reactive mode to analyze and prove their obvious worthlessness or harm? [Read More]