Computer science now counts as math credit in most states – is this a good idea? Computer Science |
- Computer science now counts as math credit in most states – is this a good idea?
- On the nearest neighbor method
- OpenCV-Inspired Kornia Is a Differentiable Computer Vision Library for PyTorch
- What do you think about an idea of proof beyond reasonable doubt for safety critical code?
- My CS professor is awful
- Good practice on class design
- A visual demonstration of the connection between information theoretic entropy and thermodynamic entropy
- Git deployment practices: is it ok to deploy your code from the master branch?
- Computer Science undergrad. Torn between what to specialize in in grad school
- Can someone post or make a consolidated list of SW companies to apply for internship/Full time ? TIA
| Computer science now counts as math credit in most states – is this a good idea? Posted: 11 Oct 2019 07:49 PM PDT |
| On the nearest neighbor method Posted: 11 Oct 2019 01:34 PM PDT I've been using nearest neighbor quite a bit lately, and I've noticed that its accuracy is remarkable, and I've been trying to understand why. It turns out that you can prove that for certain datasets, you actually can't do better than the nearest neighbor algorithm: Assume that for a given dataset, classifications are constant within a radius of R of any data point. Also assume that each point has a neighbor that is within R. That is, if x is a point in the dataset, then there is another point y in the dataset such that ||x-y|| < R. In plain terms, classifications don't change unless you travel further than R from a given data point, and every point in the dataset has a neighbor that is within R of that point. Now let's assume we're given a new data point from the testing set that is within the boundaries of the training set. By definition, the new point is within R of some point from the training set, which implies it has the same class as that point from the training set. This proves the result. This means that given a sufficiently dense, locally consistent dataset, it is mathematically impossible to make predictions that are more accurate than nearest neighbor, since it will be 100% accurate in this case. Unless you're doing discrete mathematics, which can be chaotic (e.g., nearest neighbor obviously won't work for determining whether a number is prime) your dataset will probably be locally consistent for a small enough value of R. And since we have the technology to make an enormous number of observations, we can probably produce datasets that satisfy the criteria above. The inescapable conclusion is that if you have a sufficiently large number of observations, you can probably achieve a very high accuracy simply using nearest neighbor. [link] [comments]  |
| OpenCV-Inspired Kornia Is a Differentiable Computer Vision Library for PyTorch Posted: 11 Oct 2019 10:27 AM PDT |
| What do you think about an idea of proof beyond reasonable doubt for safety critical code? Posted: 11 Oct 2019 01:14 PM PDT As a parallel, chess has an ideea of proving a position, which works very well for grandmasters. In the past I think that I've heard some major open source developers asking for "proof" that a submitted patch works in all cases. Having a justified feeling of being fully on top of the problem seems to correlate well with this idea of professional proof. [link] [comments]  |
| Posted: 12 Oct 2019 12:22 AM PDT I'm on my freshman year of college and my professor does not explain anything about coding. I have a few questions if you guys could clarify what they mean // int, string, private void, public, ext.... where do i find where these commands mean? how do i get through the year and teach myself C# coding [link] [comments]  |
| Posted: 11 Oct 2019 04:56 PM PDT So I have an idea for a 12 hour project, which is 70% math (imma do 3d stuff) and involves a framework im not the most comfortable with (pygame). Now my problem right now is that I dont want the project to take a long time so i wanna be a good boy and actually design methods and fields and all that stuff for the classes I want to be using, so after the design is over I just do the coding. Is there any resource or wikipedia page or concept or anything I missed from my classes that will help me with this? Any help is much appreciated. [link] [comments]  |
| Posted: 11 Oct 2019 09:52 AM PDT |
| Git deployment practices: is it ok to deploy your code from the master branch? Posted: 11 Oct 2019 12:52 PM PDT Hey everyone, Quick question for anyone who has used git for version control in an enterprise environment: should we use the master for deployments? I've always been told that the "master" branch is basically the gold-build. It's the version of your code that is stable. It is never merged to master until it passes all of the unit tests and whatever other quality gates you may have. I've always been told that once you have a stable master branch, you use it to deploy to your various environments. This makes sense to me. Apparently there is another school of thought, however. My boss tells me that the official best practice is to not deploy from master, instead to deploy from a different branch that called "deployment" or something and simply use the master branch as a backup. My boss believes this to be the case because apparently it's possible to completely ruin the master branch and then you'll be entirely screwed because your master is borked. I thought that the entire point is git is version control, and the concept that you can revert changes if you have to, so this line of reasoning doesn't make as much sense to me. I'd like to hear your thoughts on these two things: 1) is it better to use a deployment branch, and keep master as a backup? Or should we use master as a deployment branch? 2) is it possible to completely ruin a github repo by somehow committing something into master, without the ability to revert changes? Written on mobile, apologies for any typos. Thanks!!! [link] [comments]  |
| Computer Science undergrad. Torn between what to specialize in in grad school Posted: 11 Oct 2019 10:20 AM PDT I'm a CS junior, also pursuing a minor in Math, and in less than four semesters, I'll be graduating. I made a list of the possible sub fields of CS that I would be interested in, but I'm having a hard time choosing amongst them, and I feel like I need to do so right now before it's too late. I think it's important to tell you that while I enjoy theoretical computer science courses, I am a terrible programmer, so if I will be at a disadvantage because of my lack of coding skills in any of the following options, please do let me know 1) Computer Vision / Computer Graphics: I haven't taken either of the two courses so far, but I've found it really fascinating how we can make computers see things the way we do, and CGI in movies is something that I feel is really cool and I would love to learn about it. One thing that a few of my friends who took computer graphics mentioned was that it was very math intensive, particularly a lot of linear algebra. And that, for me, is a huge plus point because of how much I enjoy applied math and how good I am at it 2) HCI: HCI was something that I only discovered existed a few months ago but was so drawn towards, that I enrolled in the course immediately. I've always had an artistic mind, and have always been very focused on design. I designed user interfaces for an android game a few years ago, way before I knew that this was an actual field that existed. The HCI course that I am currently studying is very heavily focused on the course project, so most of the discussion that we've done so far has been about User Research, and not so much about design etc and I'm honestly starting to lose interest, but I still feel that when we finally get to the design phase, I'll enjoy it 3) Quantum Computing: In my freshman year, I took a course called Modern Physics. One of the things we were taught was the basics of quantum computing. We didn't go too deep into it. We only learnt about quantum entanglement, quantum logic gates, qubits, and the bloch sphere, but it was, for me, easily the most interesting part of the course. Out of the three options, I am most inclined towards Quantum Computing because of how relatively new the field is, and how little research has been done so far compared to most other fields. However, I have thus far not studied any other physics courses (apart from mechanics). But I really am interested in this Could you please help me figure out what the best option for me would be, because I don't have a lot of time, and I have to choose between courses, and I was also hoping I could do my senior year project in one of the three. Thanks [link] [comments]  |
| Can someone post or make a consolidated list of SW companies to apply for internship/Full time ? TIA Posted: 11 Oct 2019 06:39 AM PDT |
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