I’ve been looking for a PhD in machine learning/deep learning/computer vision/etc. for a while, but I don’t seem to find many offers.

Leaving aside the UK for a moment (where perhaps there are a bit more options, but not that many), the options in the continent seem to be very rare.

I go to the labs websites, the uni pages, contact some profs, but I’ve barely found a handful of offers in ETH, KTH, TUM and Delft. And as soon as you start being a bit picky, you end up with just one or two offers on the table to apply to.

Has anyone from Europe experienced this too?

I wonder if there are some mailing list that I’m unaware of, or if this has been the norm lately and positions go out on a rolling basis throughout the year.

It’s just weird reading about US people being rejected from so many PhD positions, but here I can’t even apply to a few.

Any advice is appreciated.

This isn’t the standard “which is better, win or lin?” question asked for the millionth time. Rather, is Microsoft’s offering of a Ubuntu-based VM for machine learning indicative of any functional deficiency in the native Win64 platform? That is, can all of the libraries/apps on the Ubuntu-based ML VM be run on Win64 native if desired? This is purely a functional question, not considering cost or any OS-specific usage aesthetics or simplicity.

The reasoning for the question is to find out how easy it would be for a Win64-based researcher without an Azure subscription to collaborate with our staff who use Ubuntu on Azure. In theory, would they be able to run the same code, with nothing unavailable that is used on the Azure platform?

Hi all

I’m looking for a good textbook, or a review paper or tutorial on advances in generalization theory and learnability in the last 20 years, i.e what has happened since Vapnik’s structural risk minimization and Schapire’s boosting?

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A lot seems to have gone on in terms regularization and resampling techniques, but I can’t seem to find much in terms of actual learning theory.

What are average Perplexity values for sentences?

I am using Flair to calculate perplexity for sentences I generate (on the One Billion Words language model) and get very low values like 4 or 5.

I thought typical perplexity values are 20+ all the way to 100+ for less fluent sentences. Is there something I am overlooking? I assume the Flair code is correct lol

Hey everyone, I’m in 10th grade and as the title says I’m working on a project for my geometry class and using machine learning as my subject. The project is basically to say how mathematics are used in a career of our choice. The career I’ve chosen to do my project on is machine learning engineer. As a part of the project, we have to conduct a short interview with someone in that career as part of the project. I don’t know anyone in real life who is involved in this field so I was hoping that if any of you who work in ML could answer some short, non-technical questions I have below. Thanks so much!

I posted this in r/MLQuestions and someone suggested I try it here instead and use a google form rather than listing the questions out.

The Questions:

https://forms.gle/mt4WoqXybMWhFMdD7

Thanks so much for taking the time to answer these! Have a good day.

Edit: Thanks so much for the detailed responses that you guys have left! I really appreciate it.

https://github.com/sepandhaghighi/pycm

http://www.pycm.ir

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Changelog :

• Adjusted geometric mean (AGM) added #177
• Yule’s Q (Q) added #193
• Compare class and parameters recommendation system block diagrams added #192
• Document links bug fixed #194
• Document modified #173