How to learn about computer network? Computer Science |
- How to learn about computer network?
- Jeff Dean AMA
- Can an optimization algorithm be “universal”?
- seL4 Whitepaper released
| How to learn about computer network? Posted: 26 May 2020 02:12 AM PDT I would like to get to know computer networks to make my life easier. I'm not interested in "web design". (choice of cabling, assembly, documentation e.t.c). I prefer to know the things I can do on my network and on the computer. I don't mind the theory. (port forwading, dns, proxy, sniffing e.t.c) Is there a separate name for it? Only cheap or free sources like books. sorry for my English. [link] [comments] |
| Posted: 25 May 2020 11:35 PM PDT Obligatory am not Jeff Dean Not sure if any of you know of him, but Jeff Dean is one of the first employees at Google and is an incredible programmer responsible for optimizing google's search engine among many other things. A few of my peers and I will have the incredible opportunity to talk to him this week in a Q&A type of setting, and I will be glad to ask him any questions y'all may have and relay his responses back to you [link] [comments] |
| Can an optimization algorithm be “universal”? Posted: 25 May 2020 06:15 AM PDT I'm reasoning by analogy with supervised learning problems in machine learning. Some methods like Neural Networks (with a sufficient number of layers) or Support Vector Machines are universal, in that they can approximate any shape decision boundary or regression function up to an arbitrary level of precision. Are there equivalent algorithms in optimization theory that can be used to solve any optimization problem (linear, non-linear, continuous, discrete, etc...), e.g. can Genetic Algorithms or Particle Swarm Optimization be thrown at any optimization problem and give us a reasonable solution? SGD is used to solve NP-Complete problems (i.e. training a neural network) - does that mean that it can be used for any optimization problem? I assume that the reverse is true: Not all optimization are universal, for example methods that work for LP or QP don't necessarily work for harder problems. If it is indeed the case that some optimization algorithms are universal, is Bayesian Optimization one of these universal algorithms? Can it be used to approach LP, QP, MIP, TSP, and NP-Hard problems in general? [link] [comments] |
| Posted: 25 May 2020 07:47 AM PDT |
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