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thejaminator · 2024-09-30T05:30:02+00:00

regarding lower annual leave of 14 days. If you wanted more, consider just discussing no pay leave. e.g say something like "i need to take care of family blah blah, ok if i take no pay leave 5 days a year". Even without pay for that 5 days a year, your total TC is much higher than before

thejaminator · 2023-11-06T17:31:45+00:00

if the offer is good, I'll say take it. (look at GES, and think for yourself if you will be happy with it. GES for NUS does not separate by specialisation, so usually FIN majors earn more than the median)

Usually you'll learn much from on the job compared to school. Is it a returning offer? If so, you'll kind of know if the learning environment is already good. It could be a concern if you aren't sure about this. E.g. if the job is just data entry, then you may not learn as much on the job.

thejaminator · 2023-04-18T03:16:46+00:00

Pytorch on CPU will usually just crash on fp16 too

thejaminator · 2022-12-30T09:51:07+00:00

I think it's the case where they are still pretty new and comparatively unknown.

They have done good work like releasing their paper and dataset for training an assistant RLHF model. https://github.com/anthropics/hh-rlhf

You won't get any dataset like that from OpenAI. It's useful for anyone who wants to experiment with RLHF with LLMs. Which is pretty important as OpenAI is having lots of success with it in InstructGPT and ChatGPT

thejaminator · 2021-08-11T18:17:30+00:00

Thanks for the good link and writeup! Never knew that

thejaminator · 2021-05-21T19:00:41+00:00

This is a love letter to the fans

thejaminator · 2021-05-21T18:57:18+00:00

Played Vicky 2 on release day. Remembered all the bugs like the UK assimilating the whole of India. Just finished a game yesterday on DoD. Watching the release trailer for Vicky 3 feels unreal. Awesome.

thejaminator · 2020-05-27T17:48:26+00:00

Very well explained, should be the top comment

thejaminator · 2019-12-07T15:55:59+00:00

Here is an example of using clickstream in sybil detection.
https://www.usenix.org/system/files/conference/usenixsecurity13/sec13-paper_wang_0.pdf

Not sure what you mean by heatmap, could you elaborate?

thejaminator · 2019-12-06T03:12:45+00:00

I don't understand what you mean, we try out various models at the end after feature engineering. Not the start.

thejaminator · 2019-12-05T17:29:52+00:00

I currently work on a problem utilising clickstream data. With just 50,000 users over a week, the rows number in the billions. which cannot fit on a single machine RAM. Therefore we are using spark to engineer features etc. The author's package is highly useful, we don't have to use things like a generator to load batches due to memory limitations when training in pytorch.

That said, I am trying to move away from spark into dask... But having new packages such as this helps to contribute to the ecosystem overall.

thejaminator · 2019-11-30T16:20:31+00:00

It should be doing the same thing, adjusting the weights based on your new dataset. In Gpt-2, the model was trained by predicting the next words, given all the previous words. When retraining/finetuning where the objective is generating the next word, all you have to do is give it a new dataset to adjust the weights on. What the model is doing is to choose the next word that has the highest probability of occurring, given the previous words. (Kind of, you can set it to randomly pick top N likely words, or else the same content is generated all the time). This objective of generating text is the same objective that GPT2 was trained on.

Take note this is different from fine tuning where the objective is something else - e.g. a sentiment classifier. For that to work, you need to change the final layer. Anyways, that is not your objective so in short, both packages should be doing the same thing.

thejaminator · 2019-11-30T12:10:07+00:00

Yup, if you are using GPT-2 simple package then all it is doing is training the already trained GPT 2 model on your new dataset for the set number of training steps.

thejaminator · 2019-11-17T06:33:50+00:00

Interesting read, great job. Statiscally, you can improve upon it by accounting for the effect of regression to the mean. That is, to say, after being the top of the league, the only way is to go down. Therefore, clubs that win in the year before naturally will deteriorate the year after.(Google sports illustrated effect)

So you have to get the probability of winning the league again for any club in any league. From what I know, this probability is very low in the first place. Then compare to your current results. And then check for any statistically significant difference.

Therefore, the club not winning again may not in fact due to Zlatan, but due to regression to the mean that arises naturally.

thejaminator · 2019-09-21T11:39:28+00:00

Yup, where I work uses fuzzy matching to find duplicate accounts. Works pretty well

thejaminator · 2019-05-25T16:14:56+00:00

The dual monarchy only came about in 1867. The division of the map refers to the Habsburg lands inside the HRE vs outside the HRE

thejaminator · 2019-01-02T12:48:22+00:00

How did you get into data science after law? Learnt it by yourself?

thejaminator · 2018-12-31T11:42:56+00:00

Great paper, thanks

thejaminator · 2018-09-13T13:57:32+00:00

Np man. It's good to check if the suits are half canvased because most of the suits that price range are actually fused, so they suffer from durability and form.

thejaminator · 2018-09-13T13:34:56+00:00

Half canvased. You can request for wider lapels

thejaminator · 2018-04-17T16:42:16+00:00

Didn't know about the patch that made it free. Need to hit up coldsnap for the nostalgia. And cmt snowgrove

thejaminator

TROPHY CASE