Tagged "python"

Learning Tools vs Learning the Craft

This is the second in a series of essays about my experience with the largest data science and programming MOOC in the world—the IIT Madras Degree Programme in Data Science and Applications. The first essay was about learning and practicing programming, and can be read here.

Four years ago, as a part of the data science diploma, I took the Machine Learning Practice course. It had two OPPEs—online proctored programming exams—worth nearly half of the course grade. We were given a list of questions, most of which were numericals, i.e. you needed to type in a number in the answer field. There was a link to a dataset, and a Google colab notebook. While the score depended almost entirely on the answer, the notebook, too, needed to be submitted as a buffer.

The Bridge of Asses: Learning Coding with Novices

Over the last few years, I have been deeply involved with the IIT-M Programme in Data Science & Applications - as a student, a mentor and an analytics consultant. The programme provides diplomas and bachelor’s degrees in data science and applications. I’m often asked why I’m so invested in the programme - especially since I’m already an experienced data scientist.

At least three people are mad at me for being in the programme. One of them thinks that I’ve unfairly claimed a higher education seat I don’t need - which would be true if this was a conventional program with limited seats. But it’s a MOOC.

A Process for Readable Code

I took a course on data structures and algorithms over the last few months. It is being offered as a part of IIT Madras’ Online Degree Program in Data Science and Programming, taught by Prof Madhavan Mukund. The program is a MOOC in a true sense, with tens of thousands of students enrolling each year. The DSA course itself is offered every trimester, and sees an average of ~700 enrollments every time. It is easy to see how communication becomes critical in running a MOOC at this scale. There is, of course, the operational and logistical communication that goes into the smooth running of the course. But communicating the content of the course is more relevant (the course is also highly interactive - in addition to weekly office hours, there are Discourse forums where students, TAs and faculty are active).

Weighted Loss Functions for Instance Segmentation

This post is a follow up to my talk, Practical Image Classification & Object Detection at PyData Delhi 2018. You can watch the talk here:

and see the slides here.

I spoke at length about the different kinds of problems in computer vision and how they are interpreted in deep learning architectures. I spent a fair bit of time on instance and semantic segmentation (for an introduction to these problems, watch Justin Johnson’s lecture from the Stanford CS231 course here). In short, semantic segmentation deals with designating each pixel of an image as belonging to a class, and instance segmentation deals with identifying which instance of a class each pixel belongs to. The following images show an example of semantic versus instance segmentation.