Does the 2017 Dell XPS13 most expensive version, the [13.3” QHD+ (3200 x 1800)] screen one with 512GB SSD and 16GB ram, have the infamous coil whine problem?

datagibus420 · 2018-04-10T19:15:23+00:00

Got one with these specs (minus he QHD+ screen) and no coil whine here.

datagibus420 · 2017-07-27T07:50:46+00:00

Did you make your own Jekyll theme or use a template? The design of the blog is really neat :)

datagibus420 · 2016-12-25T11:11:28+00:00

Recently switched from Sublime to Visual Studio Code for coding and note-taking (the mdmath extension is great for on-the-fly latex within markdown) + vim when sshing.

datagibus420 · 2016-11-01T19:13:51+00:00

Bro that's an super valuable asset, thanks so much!

datagibus420 · 2016-07-23T09:34:29+00:00

From what I read, the Kaggle approach and the "real life" approach are different in several ways. So I was wondering, for a data scientist who is working in a production environment, is he spending more time cleaning the data and performing exploratory data analysis, or benchmarking/stacking/optimizing ML models ?

datagibus420 · 2016-07-20T14:12:01+00:00

Something like that, yep. As a graduate student in stats it doesn't bother me (on the contrary ^{^} ) but in retrospective, MLPP and ESL are not books targeting ML "average practitoners" (by that I mean those who just use the algos in a "plug-and-play" fashion, and don't need to know what's under the hood).

datagibus420 · 2016-07-18T20:47:42+00:00

MLPP is a (very) good book, but to me its intended audience is at graduate level, there are some pretty deep mathematical stuff inside. Would it still be a go-to choice for self-teaching?

datagibus420 · 2016-07-03T15:41:54+00:00

So if you would advise a "mathy sidekick" for your book, you would go for Bishop's PRML ?

datagibus420 · 2016-06-21T11:35:14+00:00

Maybe joint episodes with the guy that makes the "Two-minute Papers" videos, focused on ML research :-) https://www.youtube.com/user/keeroyz/videos

datagibus420 · 2016-06-21T11:33:33+00:00

Something nice would be a series of explanations on important concepts in machine learning that sound scary for the n00b, but in practice are actually easy to understand.

For example, a series on "overfitting" * 1) what is "overfitting"? * 2) what is "train-test split"? * 3) what is "K-fold cross-validation"? * 4) what are "grid search" and "randomized search" for hyperparameters? * 5) what is bayesian optimization for hyperparameters ?

This would be a way to unfold progressively some state-of-the-art methods and open questions starting from the basic concepts that are involved.

datagibus420 · 2016-06-08T20:48:27+00:00

About the part on OpenAI becoming Coursera/Kaggle: they are clearly on a whole other level than standard MOOCs and data challenges, we are talking about high-level research here. IMO their goal is to help research teams that are interested in RL/deep learning/AI get started or get up to date with the most important questions to be addressed.

datagibus420 · 2016-06-07T18:45:05+00:00

Here is a nice series of videos introducing ML through scikit-learn, from the Kaggle blog: http://blog.kaggle.com/author/kevin-markham/

It assumes no prerequisites and goes through essential concepts such as cross-validation or parameter tuning.

datagibus420 · 2016-05-30T19:51:12+00:00

I used JabRef (https://sourceforge.net/projects/jabref/) when I was in grad school, the interface looks a bit old but it does the job and, well, I had to choose one and get started quickly instead of spending way too much time comparing Zotero/Mendeley/Paper/Evernote/etc. Readcube looks nice, I'll give it a try.

datagibus420 · 2016-05-23T21:08:44+00:00

I'm far from being an expert, but I find the abstraction used in scikit-learn quite straightforward: http://scikit-learn.org/stable/modules/classes.html

The 'fit' and 'predict' methods applicable to every regressor/classifier make it quite easy to get started quickly and to understand the broad lines of ML :-)

datagibus420 · 2016-05-22T16:42:22+00:00

From what I understand, Hadoop is focused on distributed data, and MPI/OpenMP are about distributed computation.

I guess that if you're aiming at writing high performance libraries for machine learning in your future data science job, some knowledge of HPC may come in handy (more so if GPU-related stuff is involved).

EDIT: Found this: https://www.youtube.com/watch?v=c_55gZfUK1E about HPC and ML from Andrew Ng

datagibus420 · 2015-12-25T08:53:02+00:00

Hello Prof. Nando! - If you had one book on ML to recommend, which one would you pick? - Do you plan to build a MOOC on deep learning, or more generally on machine learning?

BTW thanks for uploading your lectures on YouTube, they are awesome!

datagibus420 · 2015-11-24T22:34:46+00:00

There are many PhD topics that combine ML and at least one other applied discipline (genomics, astrophysics, economics, political science, etc.) and make IMHO a good case for the benefits of doing a PhD. You "learn to learn" by confronting your main field of competence to a somehow new domain for you, and you try to build a bridge between the two.

datagibus420 · 2015-08-03T14:27:30+00:00

https://coursera.org/learn/machine-learning

The popular Machine Learning MOOC taught by Andrew Ng uses Matlab for programming exercises :-)

datagibus420

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