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victorqribeiro
jokenpo
JavaScript2019–2019
victorqribeiro / jokenpo

jokenpo

Can a neural network predict your next move on a game of rock, paper and scissor?

2Distinct people
5Mentions
0Explicit recommenders
121GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Can a neural network predict your next move on a game of rock, paper and scissor?

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2019-03-16 15:54:52 UTC to 2019-11-14 10:15:11 UTC.

0122019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsMar 20192019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsJul 20192019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 2019

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC220
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC211
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC101

What Hacker News says

5 sources
I really like this comment. Thanks a lot for taking the time to going through the code and figure all that out. The way I learn things is by doing. Since I started college, the only way I can understand a mathematical equation is by turning it into code. The machine learning class I took was a basic one. We learned about knn, kmeans, adaline, perception, linear regression... the semester ended with a multilayer perceptron with only one hidden layer. This Dejavu NN you see in my code is like the fourth iteration of me trying to really understand NNs. Some of the things you said I already knew but didn't took the time to apply: like cross validation. Tht shuffle algorithm I didn't knew, thanks for that. Anyways, if all the criticism I got from my projects were like yours i'd be such much better now. When I learned about KNN, I made this pro
Read the rest
ject: https://github.com/victorqribeiro/budget When I learned about Kmeans, I made this one: https://github.com/victorqribeiro/groupImg When I learned about Perceptrons: https://github.com/victorqribeiro/carGamePerceptron (the training is done when you run the project) When I learned about MLP: https://github.com/victorqribeiro/jokenpo When I learned about Neural Evolution: https://github.com/victorqribeiro/aimAndShoot None of these projects are perfect, but helped me to better understand things. I did implemented a professional solution for a company using machine learning, but I used scikit-learn (back then). I have some experience with tensorflow also, but implementing things makes me fell like in control. But I know whena a solution is good enough for production and when it's not. I made clear on the README of this project that this is not at all good for production. I took the time to show every single step of the process and tell it how it is. I had limited time and limited resources, and with the little I had I made a "fun" experiment. I enjoy doing this kind of thing. I have to squish this kind of projects in my free time, cause I have a full time job and I'm finishing my final thesis (I'll defend it two week from now). Sorry if I turn this into a journal entry, but I kind got hurt yesterday when the other guy shted all over my project That's not at all what you did and I appreciate that. Thanks again.
In Show HN: Testing HN titles against a neural network

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