If you are interested in learning the intuition & theory behind diffusion and score-based models, I highly recommend this talk by Stefano Ermon from Stanford:
This has never been a big problem for me. Some clients have strict policies (e.g., only allowing you to work on their servers) while others don't care as much. In some cases, as a freelancer, you only work on a "proof of concept" prototype assuming you have access to their private data. It is their FTEs job to actually train/implement/deploy this prototype using their private data.
I do have a CPA and we did an S-corp election for 2019 tax return. This is something I didn't get to mention in the post because we hadn't filed the tax yet.
Wow, it's nice to see my post on the top page of Hacker News.
Note that I wrote this post back in February 2020 and there are a couple of things that I would like to add:
- Due to high demand, I increased my rate to $250/hour + some fixed monthly fee in April. I didn't drop any single client :)
- I'm seeing very little impact from the coronavirus. I have a client base spanning between Japan and the US in a little-impacted industry (education). Don't put all your eggs in one basket.
I did well in Kaggle's highest-prized competition[1], so I wondered if I should explore this kind of consulting myself.
The competition I was in was effectively limited to US residents though, but as a "remote" freelancer you compete world-wide, potentially with people willing to work for far less. Are there reasons your clients prefer US-based freelancers enough to justify the gap in pay?
How often will the clients find you again to do the follow-up work, e.g. new software feature requests, for the previous projects? If you refuse the requests, will this undermine the business relationship?
I don’t get what “NLP/ML for Asian language processing and language education” work is. Can someone explain a bit more. What kinds of companies do this, what kind of businesses?
An example of "language education" is Duolingo, where the author used to be an employee.
"NLP/ML for Asian language processing" covers anything that has text in CJK/other languages. I work with Japanese and lots of things taken for granted in NLP pipelines require entirely different approaches specifically for Japanese. For example, there's no spaces, so word tokenization is actually a complicated issue.
Natural Language Processing (NLP) and Machine Learning (ML) Engineer and Researcher available
I am an independent NLP/ML engineer and researcher. My clients include world-class institutions and startups such as the Allen Institute for Artificial Intelligence and RIKEN AIP. In the past, I worked as a researcher/engineer at Google, Microsoft Research, Baidu, and Duolingo. My expertise is NLP (especially for education and Asian language processing) and machine learning.
Let me know if you need help with your NLP/ML projects.
I always wanted to learn to play Go and one of the reasons was because it was the only game where computers hadn't defeated human - well, it is no longer the case and I kind of lost motivation to learn it.
I wonder what would be interesting games (intellectual sports) where computers have yet to defeat humans that you would probably be interested in learning?
"Paper-based reading yields better comprehension outcomes than digital-based reading."