What does a “productive day” in deep learning actually look like?

Hey everyone,

I’m trying to better organize my workdays now that I’m working with deep learning outside of university. At uni, a “deep learning day” might mean finishing a lab or doing a few exercises. But in the real world, what’s the pace like?

Say I need to implement a model—how much can I realistically get done in a day? There’s reading literature, checking out existing repos, figuring out what models are relevant, adapting/implementing them, maybe modifying stuff… It feels like a lot, and I’m not sure what’s a reasonable expectation for a day’s work.

How do you structure your time? Is it normal to spend a whole day just understanding a paper or going through a repo before writing any code?

Would love to hear how others approach this!