Machine Learning in Plain English Computer Science |
- Machine Learning in Plain English
- Papers and Algorithms in LLVM's Source Code
- type checked mathematical notation
- PhD research areas in the field of Data Engineering
- Request Review for a Complexity Result
- How Increasing Usage of IoT Devices Chipping in to The Volume of DDoS Attacks
| Machine Learning in Plain English Posted: 16 Mar 2019 05:35 PM PDT |
| Papers and Algorithms in LLVM's Source Code Posted: 17 Mar 2019 02:18 AM PDT |
| type checked mathematical notation Posted: 16 Mar 2019 11:45 AM PDT can someone point me to some literature on a type checked mathematical notation? i'm familiar with coq et al but i'm not particularly interested in proof checking (although i know type theory and proofs are intimately related via hott) but simply rigorously specified semantics/syntax. the inspiration for this question is this post https://ermongroup.github.io/cs228-notes/extras/vae/ which variously contains q(z|x), q(z), q(e), p(x), p(x|z), p(e) all variously referring to unconventional things and in general literature that plays fast and loose with notation. [link] [comments]  |
| PhD research areas in the field of Data Engineering Posted: 16 Mar 2019 07:49 AM PDT I studied EECS and I am currently working as a Data Engineer, i.e. I am building data pipelines. I write a lot of SQL, Python and R code, explore and deploy modern tools/packages and try to automate workflows, e.g. with Airflow. Sometimes I also do some (minor) modelling with our Data Scientists, because I really enjoy it and have a descent math background. Because it was always my dream and I love research, I want to do a PhD. Because I would like to go back to Data Engineering/Data Science after my PhD, I would prefer a field which qualifies me additionally for a position like this. Currently I think about topics in the field of deep learning (combination of CS & Math). I also thought about Econometrics, but I am not sure if I am really qualified for this. Does anybody know any other research fields (research groups as well) which are close to Data Engineering and I should have a look at? Thanks in advance! [link] [comments]  |
| Request Review for a Complexity Result Posted: 16 Mar 2019 08:51 PM PDT P versus NP is considered as one of the great open problems of science. This consists in knowing the answer of the following question: Is P equal to NP? This problem was first mentioned in a letter written by John Nash to the National Security Agency in 1955. However, a precise statement of the P versus NP problem was introduced independently by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this huge problem have failed. Another major complexity class is coNP. Whether NP = coNP is another fundamental question that it is as important as it is unresolved. To attack the P versus NP problem, the concept of coNP-completeness is very useful. We prove there is a problem in coNP-complete that is not in P. In this way, we show that P is not equal to coNP. Since P = NP implies P = coNP, then we also demonstrate that P is not equal to NP. [link] [comments]  |
| How Increasing Usage of IoT Devices Chipping in to The Volume of DDoS Attacks Posted: 16 Mar 2019 08:33 AM PDT |
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