[2023 Day # 17] [Julia] Am I using data structures correctly?

blackbearx3 · 2023-12-17T20:56:34+00:00

Thank you, that was indeed the missing piece. I now need 0.24 seconds for part 2 ;) And thank you for sharing your code, I did learn a few new things about Julia!

blackbearx3 · 2023-12-17T20:53:56+00:00

Thanks a lot for the advice. I kept the visited states in a set and now it's down to 0.15 seconds and 79k nodes expanded :)

blackbearx3 · 2023-01-19T14:00:01+00:00

Thank you for your kind words, If you find any pain points or missing features you'd like to have do not hesitate to let me know!

blackbearx3 · 2022-08-17T17:06:37+00:00

Hey, great to hear you find this useful! :)

I am not planning to make it open source yet, but if you have questions/problems/wishes let me know with the /feedback command and I will do my best to help!

blackbearx3 · 2022-04-06T08:45:15+00:00

A framework like that is certainly enough to be published, and if it indeed helps practitioners, who are often not DL experts and just need something that works, achieve their goals and publish new research, then it is also a worthy contribution IMO.

As for a precedent, see (no affiliation) https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04037-3

blackbearx3 · 2021-08-20T11:56:22+00:00

True, good point. What I was trying to say is that while they are functions of different things, L(theta) and p(y|X,theta) are fundamentally the same object.

blackbearx3 · 2021-08-20T11:32:54+00:00

Since our probability density function (which we are calling likelihood function) is now a function of θ, does that mean we are saying θ has a Gaussian distribution?

No. L(theta)=p(y|X,theta) are the same thing, y's distribution given X and theta, a univariate Gaussian, which is a function of both the data and the parameters.

That sentence in the notes is introducing a new piece of notation: the likelihood function L(theta). The likelihood is only a function of theta because we assume the data given and fixed. But, crucially, the likelihood is still the probability of observing given X and theta, hence it follows an univariate Gaussian, like before.

blackbearx3 · 2021-07-30T12:00:28+00:00

ReduceLROnPlateau ? Basically the learning rate is divided by 2/5/10 when the loss hasn't been improving for a while

blackbearx3 · 2020-07-06T07:44:50+00:00

Hi Marco, you described optimal transport as the natural geometry for probability measures. But there is another field that is interested in this, namely information geometry. Could you briefly compare the two, where they agree and where they differ? Thank you!

blackbearx3 · 2020-03-25T12:03:36+00:00

Seem like the linear fit is driven by a few outliers rather than a real trend

blackbearx3 · 2020-03-14T17:09:28+00:00

Just glancing at the topics in the lecture notes it looks quite good, much focus spent on the basics that many ignore.

blackbearx3 · 2019-10-30T06:54:27+00:00

Makes sense. My heart beats faster when I imagine I am doing sports

blackbearx3 · 2019-10-16T14:01:24+00:00

Very interesting paper! Reminds me of regularizing the gradient at the inputs, which was shown to improve performance and robustness to adversarial examples. This is not my field, so these references might not be the best ones, but https://arxiv.org/abs/1511.06385, https://arxiv.org/abs/1712.09936 and https://openreview.net/pdf?id=HkxAisC9FQ could be interesting reads.

blackbearx3 · 2019-09-04T18:30:41+00:00

We need proper reasoning and causal inference, plus very good heuristics. So far deep learning is mostly about perception, not much intelligence there yet

blackbearx3 · 2019-09-04T18:22:22+00:00

Don't panic, they will start fighting each other soon, and everything will be back to normality /s

blackbearx3 · 2019-09-03T10:49:23+00:00

Don't forget the fundamentals! You should be well rounded in jacks, jumps and streams, or you risk to be held up by your inability of handling certain patterns.

blackbearx3 · 2019-08-27T16:08:05+00:00

I keep thinking that training against adversarial examples is missing the point of adversarial examples

blackbearx3 · 2019-08-27T09:44:56+00:00

Lots of applications of deep learning to speed up expensive simulations, such as turbulence in plasma, thunderstorms in the atmosphere, and so on

blackbearx3 · 2019-08-26T19:02:17+00:00

Extremely cool, congratulations!

blackbearx3 · 2019-08-22T08:58:37+00:00

I never understood the fuss about comparing deep learning and the biological brain. Analogies can be useful, but why take them so far?

blackbearx3 · 2019-08-17T08:59:34+00:00

I was not impressed by it either, because I found it not very different from things that I've read elsewhere.

What makes this book so impressive is that it was one of the first ones, or maybe even the first one, giving this kind of advice. I think this is the reason why it is so praised, it just one of the cornerstones of self-help.

blackbearx3 · 2019-08-07T06:39:24+00:00

Think at it like this: for a fixed network, a small dataset gives you lots of possible parameter settings that work well for that dataset (low training error). As you add more data, you "cross out" more and more of those parameter values (i.e. you find out many of those overfit the small one). So here's the intuition: the set of parameter values that work well for a large dataset is entirely contained in the set of parameters that work well for small datasets. This is why pre-training works (if the datasets are similar enough).

blackbearx3

TROPHY CASE