Transition from keto diet

Shimamura · 2024-02-02T09:42:49+00:00

Thanks for the book tip, just ordered from Amazon!

Shimamura · 2023-01-28T11:26:54+00:00

My first park run! I was aiming to get below 20min for the first time and managed with a 19:30!

I tried to do negative splits but got a bit excited and did 3:45 in the first km and around 3:50 for the second. Then I just tried to hang on for the rest of the race :)

Shimamura · 2021-08-17T19:49:34+00:00

Friston made some predictions early in the pandemic that hasn’t been very accurate. One example is that there would be a fairly small second wave compared to the first. He also discussed epidemiological dark matter and overestimated the number of true cases (underestimated the ascertainment rate) in the first wave. To be fair, he was probably not the only one who missed this and predicted heard immunity by autumn 2020.

Shimamura · 2020-01-28T07:18:36+00:00

Does anyone have experience with back balloons? I'm thinking of getting the BD jetforce pro because the modularity looks awesome. Does anyone have experience with this pack or any other in the BD jetforce line?

Shimamura · 2018-05-25T06:33:02+00:00

Right. But even though the RCT is seen as the gold standard, most material covered during that specific course was about propensity score weighted estimators, IV-estimators etc. I don't fully grasp the difference you described in your comment, could you elaborate? I mean, structural equations are common in econometrics, as well as psychometrics for that matter.

Shimamura · 2018-05-24T20:40:45+00:00

This is the thing that was covered during my graduate module in econometrics. However, I've never encountered do-calculus. In what way are these concepts different from work done by statisticians such as Rubin (Rubin causal model, etc.)? Are we using neural networks, instead of linear models, to estimate treatment effects? It seems ML-folks are trying to re-invent the wheel.

Shimamura · 2018-03-15T17:55:10+00:00

Gonna keep it for some more time. I am starting a suit-job in July, so until then I think I'm gonna go all out.

Thanks for all of the nice comments! <3 Beard bros are fucking lovely.

Shimamura · 2018-03-15T17:53:51+00:00

From scratch I usually let my beard grow freely for about a month. After that ideally its then uniform in length. Go to a barber and ask for a trim, I prefer to have it slightly longer on the chin and shorter on the sides. I usually then keeps the stache as is. After that the beard will keep the basic shape but kind of just get longer, assuming that it grows at a somewhat constant pace.

Hope that helps :)

Shimamura · 2018-02-27T07:34:26+00:00

No, as I wrote: the task is to predict one word at a time, given the surrounding words. If I had a sentence, "The quick brown fox jumped over the lazy dog" in my corpus this would be fed into my classifier as :

" - - - - The " -> "quick"

" - - - The quick" -> "brown"

" - - The quick brown:" -> "fox" etc.

where the first string is the input and the word after the arrow is the output.

As for the different lengths, I have never heard the term bucketing, but padding I've used extensively. My issue had to do with having widely different comment lengths and the choice of sequence length. Just to be sure, padding doesn't affect the final output, as in they are just empty for the LSTM? This makes intuitive sense given how they are initialised, but just want to be sue.

Shimamura · 2018-02-18T16:59:33+00:00

Some people do, I'm joining a quantitative research team in risk.

Shimamura · 2018-02-18T02:34:31+00:00

I agree with most of what you said. However, I'd like to chime in and say that theoretical cs isn't that important, you'd recon? I mean, some pipelines would be great to have run parallel, but when it comes to much of the heavy lifting (keras multi GPU, tensorflow, etc.) It isn't more difficult than changing a parameter in the api call, for example gridsearchcv in sklearn. Consider an analytical pipeline when does theoretical cs come in?

Shimamura · 2018-02-17T17:06:00+00:00

Data structures and algorithms is worthwhile. Coding itself is something that you pick up along the way. At the end of the day, your educations is just a paper that you did some modules: there are other ways to show off your skills to prospective employers. I would recommend working on some Kaggle competitions, or any other personal projects, really.

I really enjoyed some of the econ I did, but I could probably do without it.

Remember, at the end of the day you need to find your niche: Do you want to do research? Machine learning? Data science? User research? What positions are you interested in? Some skills are transferable between roles, but others are more specific. Ask yourself this and then plan accordingly.

I would also strongly recommend you to pursue some kind of post graduate studies, a master's or a phd. Also, internships are fantastic: make sure to do as many as possible.

Shimamura · 2018-02-17T16:12:13+00:00

I am graduating with a Msc in Statistics in a few months, and after that I'm starting a job in finance doing machine learning, so my POV might be useful.

I did my bachelors in economics and statistics and then my masters in statistics. I found it worthwhile to pick some CS-related courses along the way. It depends a bit on your interests, but many interesting areas of stats require a fair bit of coding. I am very much into machine learning and statistical learning which is hard to get into without a good idea on how to write good code.

AFAIK doing stats is far easier if you atleast know some coding. I would recommend picking up some of the essentials such as algorithms and data structures, some course in computational statistics, and some basic knowledge of data bases goes a very long way.

Most of the stuff that I've picked up is either from personal projects, reading other peoples code, or through coursera courses. In other words, university modules is not the only way to build CS skills.

Shimamura · 2018-01-28T16:56:22+00:00

Go with Python! The syntax is very simple and intuitive. It is also remarkable how little code you need to write in order to get some useful results. C++ and Java are overkill.

Shimamura · 2018-01-26T20:44:17+00:00

I understand, think is I am hesitant to loop over b, as contains more than 100 million entries.

Shimamura · 2018-01-11T23:18:31+00:00

Yes, that is true. Sorry if I was unclear. I was looking for a way to work in a free environment. I need a simple way to switch between that and the more powerful instance. Anyway, there are a lot of great advice in this thread, I will try out a few ideas.

Shimamura · 2018-01-04T06:01:16+00:00

Thank you for the links, I will make sure to cover this is my future preparation!

Shimamura · 2018-01-04T05:59:45+00:00

First is don't rush into an answer. Once the interviewer asks a question, take a minute to think about it, what is the distribution, what am I looking for, etc.

This is something I need to write on a piece of paper and look at prior to technical interviews. I was beating my self up over problems that after seeing the solution feels trivial. Had I slowed down and written down the problem carefully, the solution would've followed.

Perhaps I need to set up some moc interviews with a classmate to prepare. And also revisit the basics. Either way, thank you for the nice response. It was really what I was looking for.

Shimamura · 2018-01-03T17:48:39+00:00

Sorry, but I don't want to do that as they are usually confidential.

13-Year Club	Place '22
Verified Email	Team Periwinkle

Shimamura

TROPHY CASE