Deepnote is part of the Github student developer pack

The SF3 Machine Learning course is a short and intensive laboratory course for 3rd year engineers, accounting for 50% of the students’ course load during that time. The students code up their own ML algorithms to achieve solutions of a set milestones, and submit a 15-20 page report. The challenge for us was setting up an environment where students can complete the technical parts.

Professor Christopher Brooks had a goal to create an online learning environment for students of his class. So, in addition to teaching at the university, he set up weekly online meetups which students can join either via Zoom for an interactive experience, or on Twitch for rather passive watching. Chris screen-shares his Deepnote project, and over the sessions, the group learns to predict the next Hart Trophy winner in NHL.

Deepnote helped sports analysts become more effective by unlocking a whole host of new abilities — easily build ETL pipelines, schedule web scrapers, and and build interactive dashboards.

Mars rovers require a high degree of autonomy as the long signal round trip time makes real-time control from Earth impossible. Pavol's task, as part of the Mobile Robot Systems course at Cambridge University, was to correct for sensor drifts of the Curiosity rover, using visual odometry (VO) and a camera feed.

Deepnote is like Google Docs for programming. You can work real-time in the same notebooks without getting merge errors and other struggles you are typically confronted with when programming in a team. The platform allows you to see what your teammate is working on and what his/her results are on the spot.

Deepnote was a huge success for Dr. Tedrake's Robotic Manipulation course. He tells us that student projects have been getting better each year, but after switching to Deepnote, the improvement was dramatic.

It was the beginning of a semester, and Luděk's team with three other classmates had four small data science projects ahead of them. Each project was based on different data: categorical, numerical, image, text. After the first week of trying to collaborate via Github and daily running into painful merge conflicts, they looked for a platform that would allow them to focus on the actual data science work instead of resolving merge conflicts.

Deepnote's philosophy — that notebooks are not just for data scientists but for teams, aligns with and enables our vision of active learning . Their platform supports our unique teaching strategies focused on personalization, immediate feedback, accountability, and peer engagement.