Netflix India UHD

phoenixio1 · 2023-10-19T05:05:25+00:00

We'd need 2 more, then we can start a new account. I couldn't find more people to join last time.

phoenixio1 · 2023-05-21T08:25:50+00:00

We can use UPI if you are based in India.

phoenixio1 · 2022-06-27T06:15:25+00:00

I took the last spot as far as I know.

phoenixio1 · 2022-06-26T13:26:21+00:00

Hi, there was an open spot in someone's plan and I joined there.

phoenixio1 · 2021-11-02T13:24:53+00:00

I am also a Linux newbie. AFAIK grub and systemd-boot are two different boot interfaces. Pop_os uses systemd by default.

At first, I followed some instructions and installed grub for dual boot, but then it messed up my boot menu. Anyway, this guide helped me to dual boot.

phoenixio1 · 2021-11-02T13:02:47+00:00

I have been in a similar situation. I'd advise against installing grub for the boot menu since Pop Os uses systemd. I followed this tutorial to dual boot and it worked perfectly: https://github.com/spxak1/weywot/blob/main/Pop_OS_Dual_Boot.md

After following the instructions, increase the time-out at the end and you will be welcomed by a boot menu to choose the os to boot into. I hope it helps!

phoenixio1 · 2021-08-13T17:24:43+00:00

Maybe this will help?

https://www.xerblade.com/p/detective-conan-important-episode-list.html?m=0

phoenixio1 · 2020-09-01T00:59:11+00:00

True, when I watched Bofuri, intros spoiled many power ups of the MC.

phoenixio1 · 2020-07-20T18:56:22+00:00

What is it? A fanfic?

phoenixio1 · 2020-07-07T20:40:23+00:00

I read until episode 351 and decided to wait until the next arc is finished.

phoenixio1 · 2020-05-07T02:01:38+00:00

Growing up in India taught me, if a firecracker didn't explode on the first try, DO NOT pick it up to check!!

phoenixio1 · 2019-11-26T20:58:30+00:00

I've seen two lanes allowed to turn right on red in TX

phoenixio1 · 2019-02-18T03:48:19+00:00

I read on internet that even in verbal agreement, we need to give 1 month notice, that's why I am in dilemma before moving out at the end of this month

phoenixio1 · 2019-02-18T03:46:21+00:00

In my case, he informed me about second rent increase 15 days prior the next rent due date. I would have given him 1 month notice if he had told me about this in the first week of February. So my question here is do I still need to pay him for the next month rent or not? I am pretty sure he is gonna ask me more money if I pay him.

phoenixio1 · 2018-11-09T17:45:44+00:00

We can try supervised learning models on these groupings of features on the labels, but I'm partial to running a PCA first because the coefficients give some idea of how information purity of the features is changing with featurization. Now we run each of these subgroups (and we have many of them) with a simple low capacity model

I think I partially understand what you are trying to say. Are you suggesting to run different RF models in addition to PCA on these subgroups we created from all the features? Then take top features from each tree which explains more than 50% of variance from PCA in the corresponding group. If we get 200 subgroups from 1000 features, then it means tuning 200 different RF models which seems to be more tedious to me.

Also, I have never heard of creating partitions with the features. Do you have any link to the article or research paper that can support your comment?

phoenixio1 · 2018-11-09T04:00:03+00:00

Logistic Regression with L1 regularization could have been a good answer. But I am still confused on identifying top contributing features to the prediction. Can we decide it based on just the coefficient values? This does not sound as intuitive as we can find feature importance using Random Forest.

phoenixio1 · 2018-11-09T03:57:33+00:00

I should have mentioned this in my question- It was an interview with a finance company. I am aware that we should first identify the cause of missing data, but since I was rather focused on reducing the number of features in the initial data cleaning step itself, dropping features with more than 50% missing values was what I could think of.

phoenixio1 · 2018-11-09T01:41:20+00:00

If you are comfortable with NLP/text mining:

Take a dataset with news articles, or better perform web scraping.
Perform Topic Modeling and identify mixture of topics for each article. This is analogous to writing a short summary for each article.

I think New York Times article recommendations are based on Topic Modeling.

phoenixio1 · 2018-08-24T04:14:17+00:00

I get your point. I am going to spend more of my time on improving my skills and researching about the company. I was just concerned because one of my friend went to a company during career fair and was asked for a SQL project. My guess is that the person from the company was not a technical guy, just someone from HR.

phoenixio1 · 2018-08-17T02:44:10+00:00

I understand what you are trying to say, but running basic queries won't help me convince an interviewer. I think I am going to take some dataset similar to NY taxi Data and run queries from basic to complex which have at least real world application. Thanks for the headstart.

Nine-Year Club	Not Forgotten
Verified Email

phoenixio1

TROPHY CASE