Magic: The Gathering is Turing Complete Computer Science |
- Magic: The Gathering is Turing Complete
- EXACTLY HOW is Discrete Structures (Discrete Maths) useful in future compsci/ programming in general?
- 10 things every programmer should know
- What does it mean to say that we can represent 2.709 significant decimal digits with a 9-bit binary mantissa in floating point?
- Goldman sachs interview question
- Top 6 Data Science Use Cases in Design | ActiveWizards: data science and engineering lab
- Whats a practically efficient 1 bit secure-hash of binary-forest?
- undergrad vs masters ? worth it for technical knowledge ?
- [Lambda Calculus] Difference between strong and weak normalization ?
- Google Accelerates Quantum Computation with Classical Machine Learning
- C# is NP-hard
- Hugging Face Implements SOTA Transformer Architectures for PyTorch and TensorFlow 2.0
| Magic: The Gathering is Turing Complete Posted: 04 Oct 2019 05:00 PM PDT |
| Posted: 04 Oct 2019 07:27 AM PDT Hi there. I keep hearing from this subreddit and cscareerquestions that a strong understanding in DM/DS is essential to being a strong programmer. But no one really explains why it's important and exactly what is important (since DS is huge and different unis cover different topics). At first, when I was learning sets, universal quantified statements, functions, recursion I was really motivated and it was fascinating to be able to break down logic as I could totally see myself using this way of thinking like a programmer. But now I'm at number theory and mathematical induction and my godawful professor has essentially taught us in a way that emphasizes memorizing formulas as we blaze through the material. Furthermore, I don't see how any of this has to do with programming/CS any longer and I'm finding it very very difficult to stay as motivated as I was in the beginning (this may perhaps be due to the fact that I was studying/going for class 60 hours a week at first due to the pace of my Uni). I still keep up with the classes and am able to answer 75% of assignment questions, but I've lost any motivation of mastering the material. This is especially problematic because I have no motivation to revisit these topics before exams. How do these proof techniques help me as a programmer? I keep hearing something along the lines of "oh it helps you INDIRECTLY think like a programmer" without specifying exactly how. To me, that's like someone telling a mathematician that they should take physics because it helps them indirectly think of math better; sure maybe a mathematician would benefit slightly from taking physics - but it seems like an incredibly inefficient way of strengthening their math. If you have any insights as to why this is important to master, I would sincerely appreciate hearing them. I've heard people say it helps with understanding algorithms, but again do so without expanding exactly WHAT helps with algorithms and data structures (so at least I can hone in on them). And I've met programmers who landed internships at B4 companies via algo whiteboard interviews who weren't good at DM/DS. Note: I enjoy all my programming modules so far and don't see myself taking any in the future that are extremely math heavy (eg Machine Learning). My maths background is good but far from stellar. Maybe I'm just being naive and jaded, so thanks for your patience with reading this. [link] [comments]  |
| 10 things every programmer should know Posted: 05 Oct 2019 01:33 AM PDT |
| Posted: 05 Oct 2019 12:33 AM PDT Lets say we have a 16-bit floating point representation with a 9-bit mantissa. It's my understanding that the 9-bit mantissa gives us 9 significant binary digits. Looking at the IEEE 754 specification, I find that "Decimal digits is digits × log10 base. This gives an approximate precision in number of decimal digits." By that logic, 9 * log_10(2) = 2.709 significant decimal digits can be represented by a 9-bit mantissa. What does this mean in practical terms? Is it not possible to express a 3 digit decimal number with 9 bits? How does that explain, for instance, 1 1111 1111 = 511? Clearly there are 3 significant decimal digits, and that's the case from 100-511. How can I interpret the 2.709 figure? [link] [comments]  |
| Goldman sachs interview question Posted: 04 Oct 2019 10:47 PM PDT So I had an interview at GS. The interview started fairly easy but I think I fucked up at the start. They asked something like the following: Some_Array=[1,2,3] Some_value= 2 Write a function that returns a booleans value, true for when the value is in the array. Using python I thought of the built in function "in" to just check. So something like "if value in array: return ..." It was correct however that algo runs in O(n) which isn't optimal I think. What would be the most optimal way to check? (I do not remember if the array was ordered) [link] [comments]  |
| Top 6 Data Science Use Cases in Design | ActiveWizards: data science and engineering lab Posted: 04 Oct 2019 08:22 AM PDT |
| Whats a practically efficient 1 bit secure-hash of binary-forest? Posted: 04 Oct 2019 11:11 AM PDT An example thats secure-hash is to sha256 the concat of the 2 input hashes, where leaf is 256 0s, but that requires deep caching things other than the 1 bit per node (unless hash of 256 0s as left and hash of 256 0s as right leads to another 0, so this is incomplete theory). This can be fixed by the 2^15 bits below THE LEAF being pseudorandomly generated. An example thats almost secure-hash but not practically efficient is isEven(sha256(the approx 32k bits of 16 bits deep into binary forest)), where there is only 1 leaf whose descendants are all 0s to any depth.... except that near the leaf before it gets deep enough there will be some duplicates, but instead imagine it as an infinite forest where every node's bit is strong-pseudorandomly generated from the bits within depth 16. An example thats secure-hash is xor of very very many sets of MINORITY_BIT of 3 paths (such as left left left right left right right left, and then NOT or AS_IS, of each of 3 bits), to 16 depth. Is there an efficient way to represent each node as just 1 bit, without having to store any other bits than that same algorithm's output bit computed for descendants recursively? I'm interested in this for practical use in functional programming where each object is either a funcall pair or leaf in the form of type:content such as "image/jpeg:...bytes of jpg file", which may some day help in formal-verification of the whole Internet by... f(;op:funcParamReturnElseInfiniteLoop x y z) //call x on y halts on z f(;op:funcParamReturnsomethingotherthanthisElseInfiniteLoop x y z) //call x on y halts on something other than z f(x y) //call x on y halts Godel-incompleteness compatibly, it can represent the statement of x called on y halts (on z or anything other than z) but cant represent the statement x called on y does not halt. [link] [comments]  |
| undergrad vs masters ? worth it for technical knowledge ? Posted: 04 Oct 2019 05:10 PM PDT Currently attending a no-name online government university for computer science. I have the option to transfer to a more recognized school, but my graduation would be pushed back by at-least 1 extra year, and at most 2 extra years. With my current school i can graduate in the next 6 months or longer if needed, but with a transfer i would need to start school for at-least a year more starting from 2020 September. This made me think, if i am going to spend another 1-2 years in school for a brand name undergrad why not just spend the same amount of time and graduate from the no-name right school now, and then apply for a masters? A masters would take 1-2 years so the same amount of time as transferring now to a recognized undergrad. In the master's option i will still have a no name undergrad degree. So what is more important ? a masters from a good name school and an undergrad from a no-name school ? OR a undergrad from a good name school ? [link] [comments]  |
| [Lambda Calculus] Difference between strong and weak normalization ? Posted: 04 Oct 2019 01:13 PM PDT I googled that and didn't understand the answers.... As far as I understood, if we can reduce the term with finite steps then it's "normalizable". In my lectures I have : We say that a term t is strongly normalizable if there exists no infinite sequence (t_i)i∈N of λ-terms such that: but no mention about the weak.... (sorry for the bad math expressions) (it would be preferred if you use Krivine's notation to explain but doesn't matter much) Thanks in advance [link] [comments]  |
| Google Accelerates Quantum Computation with Classical Machine Learning Posted: 04 Oct 2019 11:44 AM PDT |
| Posted: 04 Oct 2019 02:54 PM PDT |
| Hugging Face Implements SOTA Transformer Architectures for PyTorch and TensorFlow 2.0 Posted: 04 Oct 2019 08:07 AM PDT |
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