Jump Rope + AI! Made this application on top of OpenPose (Python). Link to the Medium tutorial and the GitHub Repo in the thread. Computer Science |
- Jump Rope + AI! Made this application on top of OpenPose (Python). Link to the Medium tutorial and the GitHub Repo in the thread.
- DFS Gradient Themed Maze Generation [Link to blog explaining concepts]
- UNDERSTANDING NOISE IN QUANTUM COMPUTING: In an interview with our editor, quantum expert Jessica Pointing explained the different types of "noise" affecting Quantum Computation and the current workarounds used for error correction; such as using "functional" qubits or algorithms.
- Doubt regarding usage of Number of nodes (vs) Number of edges in tree definitions
- Calling all high school hackers, win cash prizes from a pool of more than $1,500 at HackARG!
- VkFFT update post - support of big FFTs (up to 2^24)
- Convenient tools for measuring Big O space and time at runtime?
- I request ur guidance
- PAC Semantics w/ Continuous Labeling
- Hi there... working on a Logisim full adder circuit... I thought I got it built correctly (I’m a beginner), but it won’t work. Yes, I’m on a MacBook (please don’t roast me). Any tips on what I’m doing wrong?
| Posted: 27 Sep 2020 05:12 AM PDT |
| DFS Gradient Themed Maze Generation [Link to blog explaining concepts] Posted: 27 Sep 2020 08:23 PM PDT I recently created a customizable maze generation program based on the DFS algorithm, in C++ and SFML. I was so happy with the result that I decided to write a blog post, on its working. This tutorial is divided into 4 parts,
I start explaining with the help of an analogy, then try to convert that analogy into some kind of computeristic logic, then we have a look at the algorithms and with the help of diagrams we understand the working of the DFS algorithm and in the final programming section we learn to program and optimize it using the concept of bits and bit masking. We also try to make everything as customizable as possible, so that we should be able to change a single parameter and it would affect the entire maze. Blog : Post Link [link] [comments]  |
| Posted: 28 Sep 2020 01:47 AM PDT |
| Doubt regarding usage of Number of nodes (vs) Number of edges in tree definitions Posted: 27 Sep 2020 11:51 PM PDT More specifically, I am talking about definitions of Diameter, Depth and Height for a tree. For eg, this StackOverflow answer says that height & depth are calculated by counting the number of edges while this Leetcode question (& a few other sources) defines it based on number of nodes. Similarly, this StackOverflow question counts based on longest path(i.e. number of edges) while this GeeksforGeeks counts diameter based on number of nodes. Am I missing something here? Thanks in advance! EDIT: Also, does the diameter of a binary tree have to be the longest path between 2 leaf nodes necessarily (can it be any 2 nodes)? [link] [comments]  |
| Calling all high school hackers, win cash prizes from a pool of more than $1,500 at HackARG! Posted: 27 Sep 2020 08:38 PM PDT This event is sponsored by The Aluna Research Fellowship, Arterys, Bugsee, Dcyphr, and EchoAR. We have limited spots (30 for the AI pathway and 20 for technologies) so make sure to sign up ASAP. To register for this event, please sign up on the Devpost and fill out the google form below: https://hackarg.devpost.com/?ref_content=default&ref_feature=challenge&ref_medium=discover https://forms.gle/dyYp4VJ1WvWkDMN96 [link] [comments]  |
| VkFFT update post - support of big FFTs (up to 2^24) Posted: 27 Sep 2020 11:41 AM PDT Hello, this is an update post for VkFFT library (the last post: VkFFT). I have followed the feedback people provided me with and the main concern was the low dimension sizes support on VkFFT. So I spent some time and have implemented an algorithm that optimally splits FFT sequences in multiple uploads to the chip (known as Four Step FFT) and improved it that it groups nearby sequences to achieve non-strided memory accesses without any transpositions at all. So this update allows VkFFT to: -Do C2C FFTs of sizes up to - (2^24, 2^15, 2^15), and C2R/R2C FFTs of sizes up to - (2^14, 2^15, 2^15) . This is only limited by maxComputeWorkGroupCount parameter and I intend to increase the numbers by splitting submit calls. I will also reconfigure shaders to support double precision in one of the next updates, as twiddle factors are getting close to the levels of machine precision for single precision calculations on these sizes. -Have no transposition steps at all. Grouping nearby FFTs provides better occupancy and achieves non-strided memory accesses far easier than doing sequence shuffles. However, this breaks locality for big FFTs, as Four Step FFT algorithm requires transposition after it to return data to the original order. It can be done with position shuffle routine in all shaders and an in-place transposition shader launch, not yet fully implemented though. This doesn't matter for convolutions - they return to the input ordering automatically. -Big FFTs and convolutions were also tested on a real scientific Vulkan application Vulkan Spirit for result correctness. The update to it will be uploaded to GitHub soon. -Big FFT sequences still retain 5-20% performance advantage over cuFFT. There are a lot of parameters that can be tweaked between different FFT sizes and GPUs to achieve even better results and this is something that can be done in the future - get specific launch configurations for different machines. Benchmark is added as a sample 4 in Vulkan_FFT.cpp and benchmark_cuFFT_big_FFTs.cu scripts, I will update comparison plots later. -A radix 3, 5 and other support can now be fairly easy implemented, due to the fact that VkFFT has no in-place transpositions anymore. -I have added shaders that overutilize register file instead of the shared memory for 8k and 16k x-sequences. This algorithm allows to improve performance on high-end cards and I will extend it to y and z sequences in the future. Hope this makes VkFFT more useful to the community! GitHub link: https://github.com/DTolm/VkFFT [link] [comments]  |
| Convenient tools for measuring Big O space and time at runtime? Posted: 27 Sep 2020 04:32 PM PDT As I'm solving various algorithm challenges during interview prep, I find myself wishing there were an app I could drop various algorithm implementations to see what actual runtime (as opposed to theoretical) Big O space and time growth is. Is there some package or app out there that already does this? [link] [comments]  |
| Posted: 27 Sep 2020 12:21 PM PDT Hello y'all. So I'm currently a high school junior in a program focused on computer sciences. Something all of you are well versed in. I'm currently learning SQL. I already learned Java and Python. But I want to learn more programming languages. Can any of you recommend YouTube channels or companies that teach programming. Thank you [link] [comments]  |
| PAC Semantics w/ Continuous Labeling Posted: 27 Sep 2020 09:08 AM PDT Does anyone familiar with PAC semantics have thoughts on how one could restructure PAC-theoretic analysis in terms of non-binary, continuous labels? I noticed recently that all the papers I've read in the field assume binary labels, i.e. that a sample is true or false. Of course one could always stick with binning continuous values and reverting to binary labels based on bin membership, but this loses much of the distance information that a more continuous metric would preserve. I.E. if the true value is 15.2, integer bins would simply say that both 14.9 and 13.1 are equally wrong, while a metric would allow you to label 13.1 as "more" wrong. Recommendations of papers would be equally helpful, but I thought it might be fun to get a discussion going as well. [link] [comments]  |
| Posted: 27 Sep 2020 12:34 PM PDT |
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