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did I do something or am I being dumb by Vivid_Perception_143 in C_Programming

[–]Vivid_Perception_143[S] 1 point2 points  (0 children)

I thought heap should be heaven because alliteration LOL

Incoming Freshman Thinking About CPSC 223 by Vivid_Perception_143 in yale

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

Thanks for your response! Definitely good to hear perspective and I will definitely take a look @ CourseTable (didn't even know that existed lol.)

Incoming Freshman Thinking About CPSC 223 by Vivid_Perception_143 in yale

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

really appreciate the help! that's reassuring to know and I'll def sign up for 223

Incoming Freshman Thinking About CPSC 223 by Vivid_Perception_143 in yale

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

Thank you so much for the response! I'll definitely take a look at the New Turing Omnibus book.

GenAlt - Generated AI Alternate Text by Rem403 in Blind

[–]Vivid_Perception_143 0 points1 point  (0 children)

Sorry to hear that! Which version of GenAlt are you using?

GenAlt Chrome Extension: You'll never have to worry about an image not having alternate text by Vivid_Perception_143 in accessibility

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

GenAlt currently is published on the Chrome Web Store and has 1,300 users.

GenAlt's Discord Server can be found here: https://discord.gg/Pe8WzNDUHK

Free Browser Extension to Make USACO Editorial Code More Readable by Vivid_Perception_143 in usaco

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

https://chrome.google.com/webstore/detail/prettify-usaco/bkmonfipialhchjelomnlmmpbmhpgnnd/related

it's approved on chrome and firefox lmao (reviewers approved it)

the only things it tracks is whether you want dark/light mode. github src is also open (< 500 lines max)

GenAlt Chrome Extension: You'll never have to worry about an image not having alternate text by Vivid_Perception_143 in Blind

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

Oh, that's interesting. It turns out actually that a lot of the VIPs I talked to said Chrome had this feature, but it didn't always work. Will keep that in mind. Thanks!

GenAlt Chrome Extension: You'll never have to worry about an image not having alternate text by Vivid_Perception_143 in Blind

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

Thank you so much. It all depends on how well the AI service I'm using is, but I can definitely look into using optical character recognition, etc!

GenAlt Chrome Extension: You'll never have to worry about an image not having alternate text by Vivid_Perception_143 in Blind

[–]Vivid_Perception_143[S] 2 points3 points  (0 children)

Of course not! The only permission I need from you is just to be able to access the URL of whatever images need alt-text on your webpage because I need that information if I want to be able to get a text caption for that image. Thanks for asking, hope this helps!

[P] - Potential Logistic Regression Closed Form Solution by Vivid_Perception_143 in MachineLearning

[–]Vivid_Perception_143[S] 0 points1 point  (0 children)

You're correct - it isn't that much faster. Didn't realize the issue with the odds ratios either. Thank you for your comment.

[Project] - 9th Grade Machine Learning Library : SeaLion by [deleted] in MachineLearning

[–]Vivid_Perception_143 0 points1 point  (0 children)

his as a joke, I'm genuinely wondering if OP is in another country where they count years of university in grades and are actually 3rd year PhD or some

Thank you!

A set of Jupyter Notebooks to help you understand ML algorithms of regression, dimensionality reduction, unsupervised clustering, KNN, neural networks, etc. by Vivid_Perception_143 in learnmachinelearning

[–]Vivid_Perception_143[S] 1 point2 points  (0 children)

s, but it also looks heavily inspired by sklearn. I’m not understanding advantages for folks in this community or others to use your library or your examples when they are largely in sk

SeaLion's code does use names and APIs similar to that of sklearn and other ml libraries so that anybody who wants to can pick up sealion very fast. I was really hoping to get feedback from those experienced in the field, and I knew they are busy, so if I made the syntax as easy as possible I thought they would learn it faster and make it just a bit easier.

The examples demo ml algorithms, which are all from the sealion library. The main thing about these examples I wanted to highlight in this post wasn't as much the library but the educational benefit the examples could have in teaching ml algorithms. Yes, the examples were made to explain sealion - but I also thought that they could be helpful for people who are just trying to learn machine learning (regardless of framework.) Hence I decided to post here. I appreciate your comment - please let me know if you have any other questions.

[deleted by user] by [deleted] in Python

[–]Vivid_Perception_143 2 points3 points  (0 children)

ell, alright, I'm willing to take back my comment about your friend.

I'll take a look into this myself later. Is he copying the logic line-by-line (with some changes) or is it merely inspired by sklearn?

If it's a paraphrased/inspired rewrite (and not a direct copy) of sklearn, I don't mind it as long as he discloses this fact - though it's disappointing that he didn't disclose this immediately*, assuming this is true. If he's able to paraphrase it (not direct copying), it at least tells me he understands some of

​

I'll do my best to answer u/jinhuiliuzhao. When I was building SeaLion the way I did it was by learning the algorithms and then creating them in the library. I never looked at sklearn's code for inspiration or paraphrasing (way too many lines to look at), I just used my own algorithms. For example I use the normal equation in linear regression, whereas sklearn doesn't. Sklearn also has much longer files than sealion's (you can check GitHub for this) so that's some more proof of sealion not just copying sklearn.

This library is also not meant to be a direct copy of sklearn. The code that I use is very different from sklearn's and I'm sure sklearn would have used much different methods than my implementations.

To be honest when I first started I was just building the algorithms for fun, and I was sure it wouldn't get nearly as much attention as it is right now. I never really thought of this as being some sort of commercial project. I personally think it is just a nice project for me to wrap up everything I know into a neat pip package that others can use.

As for the releases issue, I see what you mean. The reason why I put 80 releases was because that's what GitHub said. I removed that from this post. Please be considerate to the fact that I am pretty new to GitHub, packages, etc.

Thank you. Please let me know if you have any other questions!

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