dbecker

> These ML-compilers are being overhyped. It's all the same trade-off as a traditional compiler

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

The people who build this all leave on October 15?

What could possibly go wrong with that?

Were you using the base model or the conversational model?

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.

I've tried AutoGPT agents about 10 times for tasks that seem like they'd be a good fit. That meaning they need to scrape current data, but otherwise they'd be possible for a person willing to put in some time to combine data sources.

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 tried this a few times about a month ago. It sounded cool, and the experience of using it was even better than I expected.

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

> That’s actually exactly how vegetarian buffets work.

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

Streamlit is awesome.

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.

This is really nice work.

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

Either way, this is cool stuff.

The author starts with

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.

If you send me an email ([email protected]) I'd love to set up a time to show you some mockups that may be the solution you are looking for.

spaces.dev is still available, in case someone wants to buy it and redirect to something like tabs.com

I haven't worked for an employer that's held that view since high school (when I bagged groceries).

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

But it's much cheaper than in-person lessons.

I'm the lead on the Kaggle Learn project, and the author of the deep learning track.

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.

People make them (and even race) self-driving cars. Check out donkeycar.com