dbecker

Funny you should say that. Because traditional compilers have been incredibly useful.

What could possibly go wrong with that?

The post says:

The base model has no prompt format. Remember that it’s not a conversational model or trained with instructions, so don’t expect it to generate conversational responses—the pretrained model is a great platform for further finetuning, but you probably shouldn’t driectly use it out of the box.

An example is "create a table showing the current ratio of rental/sales prices (on a per square foot basis) for residential properties in the 10 most populated counties in Colorado."

I'm still yet to get a reasonable result from trying this.

I'm glad it was reposted so I get another chance at developing a habit of using it.

I'm going to go out on a limb and guess you don't visit many restaurants that advertise vegetarian buffets

It's nicer than sending someone static results, and it isn't much more effort.

And vastly better than sending a notebook to someone unless you expect them to modify the notebook a lot.

And learning time to make Streamlit useful for a small internal apps is probably ~15 minutes for most people.

If you had a mechanism to subscribe for future updates, I'd do it.

Either way, this is cool stuff.

The data science world may reject me and my lack of both experience and a credential above a bachelors degree

More likely the data science world will reject him because he is so confident a field he has so little experience or knowledge of.

Data scientist is a profession rather than the name of an academic field. So data scientists' job is to solve practical problems. That involves a lot more than class assignments, and in some cases involves using machine learning to maximize predictive accuracy (because common ML models like gradient boosting capture interactions and non-linearities in a richer way than the GLM models the author is familiar with).

Their argument "that's a garbage model because we can't reasonably interpret underlying parameters," is replacing their personal criteria above what is needed to solve some problems.

They can blame it on only having bachelor's degree. But the real problem is the belief that a bachelor's degree taught them everything there is to know, and those in the DS field are ~ idiots who got lucky enough to be paid more.

I'm skeptical any successful business treats high-skilled employees that way.

I'm happy to answer questions here.

I agree with the commenter saying you need to do your own projects to understand these topics.

Our deep learning track is meant to be the fastest path to knowing enough to do your own projects. You can do the entire track, including the hands-on exercises, in a single sitting.

We won't make you an expert in an afternoon, but you'll know enough to start doing your own projects. For most people that's also the point where Deep Learning becomes fun enough that you'll find time to keep learning.

Kaggle Learn is still in a very early stage. We'll add more lessons soon. But we'll stay committed to the goal of getting you up-to-speed quickly, so you can take on your own projects.

In this case, the find some non-zero effect, but call it zero because the difference was not statistically significant.

That's likely a reflection of their small sample size rather than evidence for the null hypothesis as the title suggests.

But it's hard to know, or even have an intelligent discussion about it, since the paper itself is behind a paywall.

Trivially simple example here: https://www.kaggle.com/dansbecker/transfer-learning-scratch-...

This page was last modified on 10 November 2014

We ate $1500 in damages on one of our first visitors, and we're off the platform now. The whole experience was a real bummer.

The first 2/3 of an undergrad curriculum at most universities is all macro and micro theory. So, most of what an undergrad sees is prove this or solve that.

But far more econ faculty focus on empirical work for their personal research.

It's possible that we focus on theory because it spreads economic orthodoxy faster than empirical work. But economists spend much less time doing theory when they are outside undergraduate classrooms.

Do you have an explanation for why Uber has been outed with so much more shadiness than most (if not all) other billion dollar tech companies?

Have they done more? Are they worse at PR? Something else? Do you dispute that they have been exposed for more shadiness?

You just compared good customer service to the largest genocide in human history.

Predictive accuracy is measured on 1000 samples, not 20.

I hadn't imagined someone would argue that's not a meaningful difference.

Though the difference is statistically significant too.

Maybe you aren't familiar with deep learning: but this isn't "trying really hard." This is doing basic stuff that anyone using deep learning probably knows.

And deep learning doesn't just "do as well" as the simpler model. It does meaningfully better at all sample sizes.

The author explores sample sizes up to 85, and then suggests this is the relevant range except at Google, Amazon, Facebook, etc.

But the VAST majority of people considering deep learning have sample sizes between those extremes. Results on small samples are interesting, but it's disingenuous to market this as typical of the world outside Google.

The area I live in just had a huge hail storm. Most roofs in our neighborhood will require repairs. That's a bummer with asphalt shingles, but it would be a lot worse with an $80k roof.

Most people won't think about those risks, but over the time period you need to consider for a roof, it's a big risk in most places.