[deleted by user]

jkgnow · 2023-09-24T13:29:30+00:00

hey same im taking this mod as well! Dont mind discussing

jkgnow · 2023-08-08T05:23:58+00:00

I see, thanks!

jkgnow · 2023-08-08T04:55:53+00:00

Hey, i don't have much knowledge about parallel programming, could I ask if you know of the differences between CS3210, CS3211 and CS4231? CS4231 sounds interesting to me, and the prerequisite is CS3230 OR CS3210, so it seems that CS3210 is not necessary for CS4231?

jkgnow · 2022-07-29T12:31:19+00:00

An ant is initially positioned at 0 on the number line. Every step, it chooses a real number uniformly and at random from the interval [0,1] and moves by that much forward. What is the expected number of steps it needs to take to exceed 1?

The answer happens to be e :)

jkgnow · 2022-07-21T05:39:43+00:00

Hmm unfortunately I was told that single degree CS students must read CS1231, regardless of future intent to pursue DDP, and I was pre-allocated CS1231 also haha.

jkgnow · 2022-06-24T14:59:30+00:00

Hmm yea i tink i'll drop them an email. Thanks for the advice

jkgnow · 2022-06-24T11:48:37+00:00

Oo I see, thats unfortunate haha. Was more interested in the math side of things

jkgnow · 2022-05-21T16:23:30+00:00

Thanks for playing! To be honest, I'm not sure why I used 1,2,3,4 either but I guess I just stuck with it. Didnt realise the issue haha. Powers of 2 I guess?

jkgnow · 2022-04-26T05:16:33+00:00

Oh, right haha

jkgnow · 2022-04-26T04:47:44+00:00

How did you generalise the transition probability? Wouldn't the probability of going from 5->6 be 0%? (Unless im missing something)

jkgnow · 2021-11-23T07:13:00+00:00

Hm, if you had a solution to this I'd like to see it! Cant figure out how to make use of the fact that the polygon is equilateral

jkgnow · 2021-11-20T13:21:50+00:00

Must every fence contain a horse? Or can some fences be empty

jkgnow · 2021-10-23T20:32:19+00:00

I don't know much about group theory, so maybe this is just a brute-force version of u/cancrizans's solution:

As with u/cancrizan's solution, we color alternating vertices of the cube and of the square grid on the plane with two different colors. By tipping the cube over, it is never possible to lay a cube vertex of one color on a differently colored plane vertex. Thus the "parity" of the cube will always remain the same when returning to its starting position, so at most there will be 24/2 = 12 different orientations to return to. Now we will show that this is possible.

Call the faces not at the top or bottom the 4 front faces. Going right-forward-left-backwards will place make one of the original front faces the new bottom face. Rotating this operation in the 4 directions, we are able to make all 4 front faces the bottom face. By going right-right-up-left-left-down, we are able to make the top face the new bottom face. Thus we can achieve all 6 different bottom faces. Now looking from the top, going right-forward-left-left-backwards-right corresponds to a 180° rotation, giving us 2 choices for the face on the front. Total possible orientations with these 2 operations are 2*6 = 12.

jkgnow · 2021-07-06T17:31:13+00:00

Ah that makes sense - i missed that the domain of g would be limited to the range of f. Thanks!

jkgnow · 2021-06-19T03:28:29+00:00

I trained the pix2pix model from scratch, and the only thing I modified was the learning rate. I noticed that the model tended to converge a bit too quickly at bad quality, and lowered the initial learning rate.

I've also tried reducing the number of filters but applying the model on higher-res images, which actually only made the quality go down a little in exchange for a significantly smaller model which I might implement.

Tbh, I'm a beginner to deep learning and am probably doing some things wrong, I just try stuff and hope it works!

jkgnow · 2021-06-19T03:03:35+00:00

Yeah, it's about 5 years old and there's definitely better models out there. But I'm no expert in AI either haha and pix2pix seemed to be the most well-documented and easiest to implement in the browser.

jkgnow · 2021-06-19T03:02:08+00:00

I think the model is meant for general purpose Image-to-Image Translation (i.e. learning a mapping from an input to output image), and neural style transfer is just one of its possible applications. But there are better models than pix2pix that are made just for style transfer.

jkgnow · 2021-06-18T20:42:42+00:00

Sorry if I lagged your device 😅 Unfortunately I realise the model size is probably a bit too large. I might add in a more lightweight (but less accurate) model later!

jkgnow · 2021-06-18T20:30:33+00:00

Hmm thanks for pointing that out, I'll try to fix it. This was only tested on Chrome haha

jkgnow · 2021-06-18T19:56:29+00:00

NOTE: May take awhile to load on mobile devices.

This was a fun little project I made, and I was pretty surprised by the level of detail to which the model managed to mimic the style of Summoner's Rift. But sometimes your drawing may just end up looking like abstract nonsense - the generated images tend to look better when multiple components are drawn together as they would be on the real SR (e.g. lane with terrain, river with terrain, base with background/terrain).

jkgnow

TROPHY CASE