Calculus is very useful in CS. Many CS papers use it, for explaining algorithms that have a discrete implementation, for example (some algorithms for finding edges in images for example), or for probability stuff.
I agree. I have a work that pays me well now (it was hard to do the transition between what paid me well and what I liked but didn't pay because I didn't know about... I'm now at some point in-between). I'm not starving, but I have to work full time. But I'm keeping away from business stuff, at least for a while, to learn more of the things I find worth learning.
I always wanted to be a revolutionary in tech / science (but I don't little formal studies that allow me to do that from academia, I'm rather self taught), not to be rich. I think with time and focus I could have become rich by keeping my work on the old startup I left. But I felt that I was not being true to myself by doing that, and I was not liking it enough to keep pushing it further.
And what is needed for face recognition (face matching, not just detection), in the same terms? Are the same kind of tools enough for this? (from what I read, it seems so, but so far I couldn't completely believe it)
Starting with some basic knowledge of machine learning (clustering, NN, bayesian inference, etc.) and some basic computer vision / processing (edge detection, color, basic shapes), how much theory is needed for achieving that objective? (recognizing vehicles in photos, and more interesting objectives: extracting 3d structure from a single 2d image).
- check mail at most once an hour when programming
- keep your mail unread if it requires an action, don't look back at read mails in your inbox (send old inbox mails to some other folder once a week for example)
- try not to keep old mails without actions for too much time... if a mail will have to wait, send it to another folder so that your inbox is always small and easy to check (around 10 unread mails at the end of each day)
- send mails that require an action to a todo folder; reply mails that require an answer immediately before you forget them
- learn a lot of languages, like everyone else does... a lot of this learning will be useless with time
- learn, for example, a lot about computer vision... the road for this is not "pretty finite" and as you learn more about this, you can work on more and more impressive projects every time (making your value bigger)
This is how I see it, and I don't think the first path is worthwhile. (Yes, I learned a lot of languages and frameworks too... but at a point I started asking to myself: why care about most of it? the real stuff is not this)
I think the Linus Torvalds example was a good one. Or John Carmack. Do you see them talking about a lot of languages and frameworks or do you see them learning and doing new stuff?