CompSci Weekend SuperThread (January 10, 2020) Computer Science |
- CompSci Weekend SuperThread (January 10, 2020)
- ‘Brains Are Amazing’ — Neuroscientists Discover L2/3 Human Neurons Can Compute the XOR Operation
- Anyone know the name of a fictional book about disgruntled software engineers and their struggle against an organization filled with massive complexities regarding practices, tickets, code reviews, documentation, project flow, etc?
- PracticalAI 2.0 | ‘Learn, Explore and Build’ ML Models Online
- Advice on doing well in classes?
- Where can I learn data structures and algorithms on my own?
- Degree choice for comp vision/ml?
- Fully managed web-based platform for running software jobs and experiments
- How come the time complexity of breadth-first-search is O(V+E)? the number of vertices.
| CompSci Weekend SuperThread (January 10, 2020) Posted: 09 Jan 2020 05:04 PM PST /r/compsci strives to be the best online community for computer scientists. We moderate posts to keep things on topic. This Weekend SuperThread provides a discussion area for posts that might be off-topic normally. Anything Goes: post your questions, ideas, requests for help, musings, or whatever comes to mind as comments in this thread. Pointers
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| ‘Brains Are Amazing’ — Neuroscientists Discover L2/3 Human Neurons Can Compute the XOR Operation Posted: 09 Jan 2020 10:56 AM PST |
| Posted: 09 Jan 2020 01:29 PM PST I remember seeing a recommendation for it (perhaps on Amazon, but I'm not sure). I don't remember much about it except that a large part of the plot included a group of software engineers in a large organization who became fed up with the maze of complexities associated with their software development. If I recall correctly, they eventually either left the organization to create their own or revolted/went on strike in some fashion. Does this ring a bell for anyone? [link] [comments]  |
| PracticalAI 2.0 | ‘Learn, Explore and Build’ ML Models Online Posted: 09 Jan 2020 03:45 PM PST |
| Advice on doing well in classes? Posted: 09 Jan 2020 11:48 AM PST Am switching from eng to cs and want to apply to grad school eventually. What worries me are many classes are proof based and I'm not used to that, most of my classes were applied (calc 1-3 and Lin Alg). I don't feel confident in that area especially bc it's very abstract. Are there resources I should follow to gain some insight or will this come with practice? I'm thinking of trying 3blue1brown. [link] [comments]  |
| Where can I learn data structures and algorithms on my own? Posted: 09 Jan 2020 10:53 AM PST I am a MIS Major currently, we are taught programming, but no CS fundamentals and I am targeting a developer job after college. Any good resources (books, online courses, etc) to use to teach my self data structures and algorithms? Thank you guys! [link] [comments]  |
| Degree choice for comp vision/ml? Posted: 09 Jan 2020 10:35 AM PST Hi am currently an electrical engineering student deciding between switching to comp eng or comp sci. I want to eventually go into research and believe that a lighter cs course load could help. However comp eng offers some core classes like signal processing. I've learned a bit on my own through online lectures (in context of using signals in comp vision, for ex 2d convolutions, sobel operators, etc.), yet that's not the same as a formal course. Additionally though CS offers courses on data science, databases, ML, and most importantly a senior thesis which I believe could be useful for a master's app. So I'm unsure, at the end of the day if I'm active and interested in the field does it really matter what my major is? Especially for two that are so similar? [link] [comments]  |
| Fully managed web-based platform for running software jobs and experiments Posted: 09 Jan 2020 08:42 AM PST Hey everyone. I'm a researcher based at a Computer Science department in Cape Town, South Africa. I do a lot of work in the field of Evolutionary Computation, and that means I'm constantly running computationally expensive code and repeated experiments. One thing I'm really tired of is the manual and tedious effort that goes into getting the code for my experiments up and running on some hardware, be it a cluster or AWS. Currently, to run large code jobs and batches, you have a few options. You can use a cluster with some sort of job scheduler like Slurm, you can use a platform like AWS and Lambda, Fargate, etc. or you can manage things manually. Then you have to track and visualise all the jobs you've run for a project (Google sheets for example). I'm building a web based platform with an intuitive UI that abstracts and automates a lot of the work needed to set up job scheduling, so that software jobs can easily be run on the cloud either serverlessly or through linked hardware. There'll also be a nice component for visualising your projects and how the jobs in them are progressing, and viewing and downloading results. I've already built a minimum version of this for running a set of repeated experiments in my research, using AWS instances. It helped a lot with running and managing large amounts of software jobs and I couldn't help but feel like others may find something like this useful. I'd love to hear what you guys think. [link] [comments]  |
| How come the time complexity of breadth-first-search is O(V+E)? the number of vertices. Posted: 09 Jan 2020 07:49 AM PST Every vertex will be enqued Once, so thats V enques. Everyone of those V vertices will need to be dequed, which should take O(n) time since we need to shift down all of the elements. Where n would be the number of vertecies currently in the queue, to simplify, lets say n=V. Since every vertex is enqued(O(V)) once and in need of being dequed(O(V)), we should get O(V^2). Is there anything I'm missing or? [link] [comments]  |
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