What is the most unbelievable or most interesting computer science paper you've read? Computer Science |
- What is the most unbelievable or most interesting computer science paper you've read?
- Help to learn how works a computer in depth
- Beginner Coding materials
- Do employers and recruiters generally give IQ tests to applicants at job interviews ?
- Julia Computing & MIT Introduce Differentiable Programming System Bridging AI and Science
- Study path for learning deep learning for music? (Ex. Project Magenta)
- Friendly introduction to RNNs, LSTMs, GRUs, and Transformers
- New Multilingual Video Description Dataset VATEX Receives Three Strong Accepts at ICCV
- Applying the relational model throughout (internal) application code?
| What is the most unbelievable or most interesting computer science paper you've read? Posted: 29 Jul 2019 09:13 AM PDT |
| Help to learn how works a computer in depth Posted: 30 Jul 2019 12:13 AM PDT I would like to know if you can help me. I only have a superficial knowledge about each part of the computer and how they are related. Can you give me resources to learn more in depth and also some simulator of computers, to see how one works in each a step and part(calling register, ALU(Adder,...),...). What do you recommend me? and thanks for your time. [link] [comments]  |
| Posted: 30 Jul 2019 05:06 AM PDT Hey all, I am trying to get into an EECS program for grad school. However, there is a slight problem: I did mechanical undergrad. And didn't get much experience in coding. What would you guys recommend for learning how to code effectively? Thanks for all your help! [link] [comments]  |
| Do employers and recruiters generally give IQ tests to applicants at job interviews ? Posted: 30 Jul 2019 12:03 AM PDT |
| Julia Computing & MIT Introduce Differentiable Programming System Bridging AI and Science Posted: 29 Jul 2019 02:36 PM PDT |
| Study path for learning deep learning for music? (Ex. Project Magenta) Posted: 29 Jul 2019 08:13 PM PDT I have a strong background in music from an academic perspective and comp sci but am not in the know at all with the current work in programming and machine learning for music. I have been strongly interested in Google Project Magenta and Max MSP for Ableton. What are some essential resources for getting my brain into this topic? [link] [comments]  |
| Friendly introduction to RNNs, LSTMs, GRUs, and Transformers Posted: 29 Jul 2019 07:28 AM PDT |
| New Multilingual Video Description Dataset VATEX Receives Three Strong Accepts at ICCV Posted: 29 Jul 2019 12:05 PM PDT |
| Applying the relational model throughout (internal) application code? Posted: 29 Jul 2019 06:30 AM PDT Often, I find myself designing and dealing with (deeply) nested data structures that represent complex configurations (e.g. webpack), application state, etc. In my experience, such structures are pretty human-friendly and easy enough to operate on as long as the hierarchy is designed with the given goal in mind. However, things tend to become awkward when one has to navigate, query and transform such structures for purposes they were not initially designed for. This got me wondering: why are nested structures commonplace when we can't know in advance which questions we'll need to answer about our data in the future? When are they appropriate and when should they be avoided? Would it make sense to apply the lessons taught by Codd in the 70s to the internal data structures of our applications? Is 'normalizing' nested application data a useful thing to do? [link] [comments]  |
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