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.
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 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.
This article is talking about diversification to reduce risk, which is unrelated to "timing the market" and which can be consistent with passive investing.
This list seemed to miss almost all modern deep learning tools... with the exception of theano, for which the version info here is years out of date. But this small detail at the bottom of the page explains it:
Funny you should say that. Because traditional compilers have been incredibly useful.