Testing vs Debugging Computer Science |
- Testing vs Debugging
- Art of the Problem just published a new video on Manifold Hypothesis using paper folding analogy
- Data Science Algorithms
- Cs50 web programming with Python and JavaScript got updated I have finished cs50 and i have intermediate python knowledge. cs50 course got updated on 1 july 2020. In 2020 version they dropped flask and focussing on django framework in 2020 version. So which one i should do.PLEASE HELP(pic attached)
- Best Java learning tips for competitive programming
- How to Learn Python For Free At Your Home
- Continuous Machine Learning (CML) is CI/CD for ML
- Linux Command Cheat Sheet
- Why not just say it's the header row? Am I misunderstanding?
- Calculus 1, 2 & 3. Now what?
- What is more valuable to know today, AI or Compilers?
- Quantum Computing
- [R] Facebook Introduces Integrated Eye & Face Model for 3D Immersion in Remote Communication
- [R] We really need to rethink robust losses and optimisation in deep learning!
- Finding the Simplest Closed Path for a Set of Points.
- Is Linear Programming the same as Dynamic Programming?
Posted: 08 Jul 2020 12:41 AM PDT |
Art of the Problem just published a new video on Manifold Hypothesis using paper folding analogy Posted: 07 Jul 2020 10:01 AM PDT |
Posted: 08 Jul 2020 01:17 AM PDT |
Posted: 08 Jul 2020 04:18 AM PDT |
Best Java learning tips for competitive programming Posted: 08 Jul 2020 04:14 AM PDT |
How to Learn Python For Free At Your Home Posted: 08 Jul 2020 03:08 AM PDT |
Continuous Machine Learning (CML) is CI/CD for ML Posted: 08 Jul 2020 02:56 AM PDT CML (Continuous Machine Learning) is a new open-source product that covers the gap between CI/CD and machine learnging: New Release: Continuous Machine Learning (CML) is CI/CD for ML (full article about the release)
All of this work on top of the established software engineering stack: Git, GitHub Actions or GitLab CI/CD, Cloud storage (S3, GCP, Azure, SSH, etc) and cloud instances. [link] [comments] |
Posted: 08 Jul 2020 01:16 AM PDT |
Why not just say it's the header row? Am I misunderstanding? Posted: 07 Jul 2020 08:40 PM PDT So many pages, reddit threads, and YouTube I feel like I'm misunderstanding some small but important point because I haven't seen it explained that way anywhere (not that I've been scouring the materials for it), and it seems the most intuitive explanation. [link] [comments] |
Posted: 07 Jul 2020 07:16 PM PDT Hope everyone's staying safe and healthy. I am a Computer Engineering Major but Comp Sci can answer this too. I'm done with all Calc, I'm now about to take classes like Programming Concepts, Electronic Materials, Discrete Structures and Linear Systems. How much of much of Calc will I need for these classes and moving forward vs. how much will I learn new math? [link] [comments] |
What is more valuable to know today, AI or Compilers? Posted: 07 Jul 2020 01:49 PM PDT Hello everyone, I'm a grad CS student heading to half of a MSc degree. Part of my post may be more suited for r/csMajors, and I have made a similar post there some time ago, but in here I would like to ask broader questions, since this is the largest CS community in reddit I think. TL;DR If you had to choose, what do you think is more valuable to know in CS today, Artificial Intelligence or Compilers/Programming Languages? For the next semester, I have to choose three courses to fullfill my requirements, two of which have already been chosen. The remaining choise stands between Deep Learning followed by Reinforcement Learning (they are offered as a sequene), or Programming Languages Implementation (aka Compilers). My track of expertise is Theoretical Computer Science and none of these courses are related to my current research, so the choise is totally based on personal interest. When I asked for advise on which to choose, you may guess that most people recommend Deep Learning, AI, etc. But recently, reflecting on what career path to follow in the long-term, I found myself thinking about what is more relevant/useful/worthy/valuable (you name it) for a CS graduate to know in the following years. Yes, AI and Deep Learning are relevant, interesting, etc and probably will remain a hot area in CS for some time. Besides, it is becoming more accessible to people to engage in the subject. As of Compilers, I think it is a very intellectually rewarding subject, but with very few application possibilities (you know, people don't build compilers every day..). Nevertheless, it is a foudational topic in CS, and I have a feeling that I would regret wasting the opportunity to take such course. The key question is: if you would have to choose between studying a current, relevant topic with wide application (e.g. AI), and a topic not as relevant and with narrow application, but highly rewarding and fundamental (e.g. Compilers), what do you think is wiser to choose? Do you think that in the following years, the skills one develops in AI and Statistics will be more valuable than the good old traditional skills one develops in Compilers and related topics? Just to emphasize, currently I don't work with neither of these subjects, but who knows what opportunities may appear in the future if one owns certain skills, right? Thank you for any feedbaack you may be able to give me! :) [link] [comments] |
Posted: 07 Jul 2020 01:32 PM PDT Hi everyone! I am very excited to start learning all about Quantum Computing this summer from QuBes camp! I wanted to know if anyone had any advice. Here is the website if you need any info or just curious. [link] [comments] |
[R] Facebook Introduces Integrated Eye & Face Model for 3D Immersion in Remote Communication Posted: 07 Jul 2020 11:45 AM PDT In a bid to push common video conferencing toward a more real-world experience, researchers from Facebook Reality Labs have developed a virtual telepresence system that uses photorealistic avatars to capture and convey the gaze and eye contact signals of real-world social interactions. Here is a quick read: Facebook Introduces Integrated Eye & Face Model for 3D Immersion in Remote Communication The paper The Eyes Have It: An Integrated Eye and Face Model for Photorealistic Facial Animation is on arXiv. [link] [comments] |
[R] We really need to rethink robust losses and optimisation in deep learning! Posted: 07 Jul 2020 11:44 AM PDT |
Finding the Simplest Closed Path for a Set of Points. Posted: 07 Jul 2020 09:00 AM PDT Dear Comrades, I am cooking a Python script which draws things with something called Epicycloids. The stuff is really cool. Check out 3blue1brown's video on Fourier Series and you will get an idea about it. One obvious requirement for the curve is that it should be closed. I managed to extract the points on the face that are necessary to construct it. But as expected, there are multiple islands. I checked out this article: https://mathematica.stackexchange.com/questions/171755/how-can-i-draw-a-homer-with-epicycloids. The best answer's author uses `shortest = Last@FindShortestTour@pts;` (Note: He is using Mathematica) which gives the best approximate curve that encloses most of the points. I am unable to find a similar implementation for Python. There is a shortestPath implementation at SciPy, but that is based on Compressed Sparse Graphs. Now I don't know a lot of Graph Theory. I don't really know how to convert a set of points into a CSG, I don't even know if it is possible. I am at a loss here. Any help is appreciated. Thank you! [link] [comments] |
Is Linear Programming the same as Dynamic Programming? Posted: 07 Jul 2020 10:02 AM PDT I'm taking an algorithms course over the summer offered by my school, but we haven't covered dynamic programming (I am familiar with it due to interview prep). However, we are learning linear programming right now, and my teacher said it was the same as dp, but it doesn't to appear to me that way. It seems extremely different to me; DP seems to make a current decision based on past decisions, but LP is just focusing on maximizing, and seems to make a grand decision one time. Any help would be appreciated! [link] [comments] |
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