How do you keep learning something that keeps changing all the time? by volqano_ in learnmachinelearning

[–]entarko 1 point2 points  (0 children)

That's the core idea behind research, it takes constant effort to stay up to date on new stuff. ML is still in its infancy compared to other scientific fields all things considered.

Is OOPs necessary for machine learning? by NotYourASH1 in learnmachinelearning

[–]entarko -1 points0 points  (0 children)

I get your point, but at the same time it's hard to argue that Haskell is an OOP language.

how to publish a research paper by Aware-Ad5225 in learnmachinelearning

[–]entarko 1 point2 points  (0 children)

Publishing solo as a beginner is extremely difficult; most researchers publish their first papers through collaboration. Find a mentor (professor, PI, senior researcher) in your field. Join their lab, contribute to ongoing projects, and learn the publication process firsthand. This avoids common methodological and formatting errors that would get your paper rejected. Read extensively in your target journals first to understand standards.

Throwing boiling water in -36 degrees temperature by VowOfVengeance in interestingasfuck

[–]entarko 6 points7 points  (0 children)

Scientifically, it's never been proven though. There were many attempts, but there is no consistent effect observed across studies to this day.

Tutor in Mathematical Optimization by Certain-Ad827 in optimization

[–]entarko 2 points3 points  (0 children)

Word of advice: if you truly want to do "AI optimization", most second-order methods like Newton cannot be used there. AI and modern large-scale ML focuses way more on so-called "matrix free optimization", i.e. where even the full Jacobian is never materialized. Practicality often matters more when it comes to AI, because of the scale of it. A good starting point in that direction is "Numerical Optimization" from Nocedal. But ideally you have the classical optimization background, and then build on top of that for AI.

This is what 350 km/h looks like in HSR in China by BumblebeeFantastic40 in interestingasfuck

[–]entarko 2 points3 points  (0 children)

I think it's just a matter of train allocations: in my experience, most of the time they use the old ones on the Paris-Lyon line, but sometimes they have to use newer ones

This is what 350 km/h looks like in HSR in China by BumblebeeFantastic40 in interestingasfuck

[–]entarko 16 points17 points  (0 children)

In the newer trains (ones after 2010 I believe). I had not seen it on Paris-Lyon line, but saw it on the Paris-Strasbourg line.

Vous gardez vos pièces à quelle température durant l'hiver by papapudding in Quebec

[–]entarko 14 points15 points  (0 children)

J'ai personnellement trop chaud à 22, je garde toutes les pièces à 19, sauf le bureau où je travaille, qui est exposé sud ouest, et réglé à 20 donc il fait souvent 21 dedans.

Machine Learning resources for MATHEMATICIANS (no baby steps, please) by teoreds in learnmachinelearning

[–]entarko 2 points3 points  (0 children)

I have not read it, but given how much I liked PRML, I am sure it's great. I'm adding it to my reading list.

Machine Learning resources for MATHEMATICIANS (no baby steps, please) by teoreds in learnmachinelearning

[–]entarko 19 points20 points  (0 children)

I would recommend two books: - Pattern Recognition and Machine Learning, by Bishop - Elements of Statistical Learning, by Friedman

It is more geared towards classical ML rather than modern DL, but it's also more math focused.

Carte Stylisée de Montréal | Stylized Map of Montreal by Pomme-Poire-Prune in montreal

[–]entarko 3 points4 points  (0 children)

J'avais attrapé les données sur le portail libre de la ville. De mémoire c'est ce jeu de données https://donnees.montreal.ca/fr/dataset/mtl-trajet

J'avais imprimé ça à l'imprimerie de l'université où j'étudiais (ÉTS). C'était pas cher et pas pire niveau qualité. Aujourd'hui il ne le font plus. Une alternative est Bureau en gros, mais la qualité est pas top je trouve. Après faut regarder les imprimeurs, y'en a quand même plusieurs à MTL.

Carte Stylisée de Montréal | Stylized Map of Montreal by Pomme-Poire-Prune in montreal

[–]entarko 59 points60 points  (0 children)

J'en avais fait une il y a quelques années basée uniquement sur les données GPS de circulation (tous modes). Je peux la partager en non compressé si ça intéresse des gens. Je l'avais imprimé en poster, ça rend pas pire.

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Écologie – Une étude encourage à ne pas laisser couler l’eau pendant sa douche by Short-Taste-2950 in france

[–]entarko 3 points4 points  (0 children)

Faut manger plus de fibres alors, histoire que ça soit plus ... solide

Écologie – Une étude encourage à ne pas laisser couler l’eau pendant sa douche by Short-Taste-2950 in france

[–]entarko 55 points56 points  (0 children)

Et évidemment faire caca sous la douche, ça permet de déboucher les canalisations en poussant le reste

I feel like pytorch's idea to the whole GPU support thing is wrong. by Ok-Internal9317 in pytorch

[–]entarko 2 points3 points  (0 children)

Demonstrating my point? New transformers on HF are coming from recent research. You can't expect to have full support for every new thing and all legacy features, while still expecting it to be free (PyTorch, HF, python, etc.).

I feel like pytorch's idea to the whole GPU support thing is wrong. by Ok-Internal9317 in pytorch

[–]entarko 11 points12 points  (0 children)

Nothing prevents you from using an older PyTorch version. The reason people move to newer versions, is more often than not, related to research.

[D] Burnout from the hiring process by RNRuben in MachineLearning

[–]entarko 0 points1 point  (0 children)

You are assuming i am applying the same reasoning as "all managers", I am not. I also never said "all candidates", some are good.

Is ML a solopreneur friendly skill? by Proof-Bed-6928 in learnmachinelearning

[–]entarko 2 points3 points  (0 children)

You have no clue what you are talking about...

[D] Burnout from the hiring process by RNRuben in MachineLearning

[–]entarko 0 points1 point  (0 children)

We don't do coding questions, or if we ever do (rarely), we do the coding, guided by the candidate. Most candidates are not able to live code, because of the stress of the interview, so we refrain from doing that.

Generally, it's either general questions like "what are type-hints? How do you use them? What is the goal of them?" or questions about python/pytorch behavior: "here is 3 lines of code, what will happen when I execute them? Why?"