For course information see: http://15462.courses.cs.cmu.edu/spring2020/
290 karma · joined February 11, 2022
Does anyone here know of something of similar good-quality OpenGL resources[1] for mathematical/scientific visualization[2]. Currently, I've started going through the source codes and documentation of manim-web[3] and CindyJs[4] to learn the basics; however, I would love to learn the fundamentals and be able to write my own visualization library (or confidently be able to extend an existing one) if/when needed. Thanks in advance.
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[1] - Personally I prefer books but I don't mind any other high-quality resources something similar to the one OP has shared here.
[2] - https://github.com/topics/scientific-visualization
https://www.openstreetmap.org/directions?engine=fossgis_osrm...
1. s/eternal/age-old/ # replace "eternal" with "age-old"
2. s/form vs. substance/signified vs. signifier/ # I think signified-signifier[1] distinction is more specific and apt compared to form-substance distinction when it comes communication.
Additionally if you're interested and want to "read" more on "business plans", I would recommend checking MIT's "Nuts and Bolts of Business Plans" course[2], at least the first lecture[3, 4] if you aren't motivated enough to go through the whole course.
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[1] - Signified and Signifier: https://en.wikipedia.org/wiki/Signified_and_signifier
[2] - MIT OCW - Nuts and Bolts of Business Plans: https://ocw.mit.edu/courses/sloan-school-of-management/15-s2...
[3] - Business Plan Basics - Lecture Video: https://ocw.mit.edu/courses/sloan-school-of-management/15-s2...
[4] - Business Plan Basics - Slide: https://ocw.mit.edu/courses/sloan-school-of-management/15-s2...
Regarding the business plan part, it seems PG does indeed read[1] "business plans": not from some written document which has static form but from a source having a more dynamic form i.e. people who are primarily responsible for writing of those static documents if/when needed.
And, about the balance sheet part, I don't really know how useful it is for the kind of businesses (early stage startup) in which PG and YC are interested and primarily invests in.
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CS is, with mathematics as its foundation, focused more on the abstract and computational aspects[2] of a computational problems/challenges. As expected, CS solutions are usually very generic and independent of any specific programming language, it presents its solution in abstract forms and along with some code (written in some programming language). From CS's point of view, the coding part of its solution is primarily[3] for demonstrating that the solution of a specified problem is computable and efficient (for some practical purpose) and can be verified independently if needed.
Now, computer programming languages are just one of the tools which helps us write those codes to "communicate" our solutions to computers and fellow programmers (who, as part of the team, need to understand in order to help developing and maintaining the software program). And, there're other alternative programming languages available which practically does the same job (though some of them are more appropriate and suitable than others, due to reasons/concerns outside the scope of a particular programming language[4]).
Having said all the above, I think I understand the core of the problem(s) you (and students in similar position as you) described here, in your post. I think I've faced similar challenges while trying to understand some non-CS course (for example, accounting and finance). Being new to any field of STEM/business/..., it's easy to get pulled into non-important areas of studies instead of focusing on the core ideas/concepts of the course. And I believe it's always course instructor primary responsibility making sure that core concepts/ideas are made very clear at the course and individual lectures level and, similarly, corresponding supporting concepts/activities which make up the course and its core concepts.
NOTE:
I've few more thoughts on the subject; however, I think, my current post is already getting too long so I stop this post, at this point.
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[1] - Btw, as you may already know, team size alone doesn't indicate that they're working on a easy or difficult problem. Sometimes it's just a consequence of some financing/time constraints or inefficient team management.
[2] - For example; data model, data structure and algorithm.
[3] - Other benefits are secondary and can be considered as bonus.
[3] - Reasons/concerns related to either CS or software engineering.