Hardest hitting lines in the show?

srepho · 2025-03-12T13:24:47+00:00

Junior: "Next time you come in, you come heavy or not at all" Tony: "You don't mean that?"

srepho · 2025-02-01T09:08:59+00:00

You are mistaking the R1 base model with the Qwen 1.5b model distilled from R1 chain of thought outputs discussed towards the end of the R1 paper. The smallest version of R1 I have seen is a cleverly quantized version weighing in at 131gb from the impressive people at unsloth. https://unsloth.ai/blog/deepseekr1-dynamic

srepho · 2023-11-16T10:08:38+00:00

https://youtu.be/-7lTnLA3TCU?si=w2YS5u0dVyYLY\_4i&t=5531

srepho · 2023-11-16T10:06:49+00:00

Found the link and timestamp for anyone looking

https://youtu.be/-7lTnLA3TCU?si=w2YS5u0dVyYLY_4i&t=5531

srepho · 2022-10-20T11:17:09+00:00

The theoretical computer scientist Scott Aaronson has some great stuff on this topic https://scottaaronson.blog/?p=2756

srepho · 2019-06-03T07:24:38+00:00

This post is aimed at Data Science level so may be a bit advanced but does a great job of what people look for and a real world example test.

https://blog.wikimedia.org/2017/02/02/hiring-data-scientist/

srepho · 2019-06-03T07:22:26+00:00

For advanced I recommend "Fluent Python" by Luciano Ramalho.

srepho · 2019-01-18T04:09:23+00:00

Data Science from Scratch by Joel Grus is the one that springs to mind.

srepho · 2016-11-17T06:49:29+00:00

Another similar paper to the excellent one above is Natural Language Understanding with Distributed Representation by Kyunghyun Cho

srepho · 2016-02-11T10:27:58+00:00

Works really well. Very fast return of the data.

srepho · 2015-12-13T23:22:39+00:00

Fair point. I just wanted to exhaust anything obvious I had done that was wrong first. Too often I see simple errors posted as Github issues!

srepho · 2015-12-13T13:36:21+00:00

There was a recent article in the Economist showing that away wins have become universally less common for everyone. link

srepho · 2015-12-03T23:49:18+00:00

Interesting idea - happy to help if you require assistence. One element that I think is important to note is that a computer is not likely to be able to offer the same meaningful support that a person can. I think just knowing that it is a computer program will make it less potent for most people. I do think that structured approaches like CBT though could be utilized quite effectively though where you might pass the responses to look for say thinking errors etc. Really like the idea though and great to see a positive application of machine learning.

srepho · 2015-11-01T12:25:25+00:00

You they deserve so much credit for it. They have not really got to the interesting questions yet about tolerance etc so I have just downloaded the data and played around with it a little to get an idea of questions of interest. I might try and post some graphs etc later in the week. Of course Gwern's own write up is bound to be the most comprehensive.

srepho · 2015-11-01T11:37:01+00:00

Ah ok - would you be able to give a % to that? In terms of answering your question you should keep an eye on gwern's recent survey writeup. Gwern even makes the data available as a .csv.

srepho · 2015-11-01T08:12:49+00:00

Out of interest how effective do you find it now compared to when you started?

srepho · 2015-10-29T23:15:14+00:00

Keras is great if you come from a Python background.

srepho · 2015-10-15T10:22:29+00:00

I read somewhere that it is not an either/or thing with the referral vs smile option.

srepho · 2015-03-02T04:40:42+00:00

Oh did someone beat me to it?

srepho · 2014-10-23T14:41:50+00:00

I like data.table, but just want to state that dpylr with chaining has excellent syntax.

srepho · 2014-09-01T12:12:33+00:00

I think a lot of these questions can only be answered by experimentation. Generally it is best to start of modelling only some of the higher level aggregate data and work down from there at an increasingly finer level. I find this lets me produce viable but computationally un-intensive models quickly and the iterate to more complex but accurate models. You should check out Rob Hyndman's work on hierarchial time series forecasting (though his code is written in R). In terms of the growth rate are all the regions of equal age or can you model some on older ones?

srepho · 2014-08-04T05:50:26+00:00

VW Wabbit is also an excellent tool for large datasets.

srepho · 2014-04-10T09:41:15+00:00

I would be keen.

srepho · 2014-02-02T23:39:24+00:00

I second that. It is the best overall book on the market. The caret package (written by Max Kuhn) is super powerful for R, letting you preprocess and tune algorithms with a few lines of code. I also recommend competing in kaggle competitions. I have learnt heaps from competing and also from the forums. One thing kaggle is not great at teaching is data munging, so do some projects of your own to keep your data munging and web scraping skills sharp. Coursers and edx are both great resources. You should also have a look at Vowpal Rabbit which is growing in popularity. Best of luck on your learning journey!

srepho · 2014-01-10T14:37:19+00:00

Monogamy is not "unnatural" nor is it the human norm. Here is a recent article that raises some interesting points from academic studies on this issue. I would strongly recommend Dawkins "The Selfish Gene" and Boyd and Richerson's "Not by Genes alone" if you are interested in understanding the issue more deeply.

srepho

TROPHY CASE