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i’ve become a friends of friends maximalist. everything I’ve done via friends of friends (hiring, dating, finding cofounders, finding new friends) has been good and every time I’ve tried other things it’s gone badly. and the same thing has been true for most of my friends
Here’s a list of my favorite blogs to stay updated on everything around building things with LLMs and the generative AI space in general:
(No specific order) https://t.co/ekRobLMSH1
i'm gonna explain this a bit. there is a "secret language" spoken by children and animals that uses the whole body as a way to send signals, this language is nonverbal and vibes-based, it's emotional, felt, and intuitive. our society is very dedicated to ignoring that signal. https://t.co/v7WwT0BdWu
Hello! As a public service, does anyone want the recipe for *real* chai — not the gross Starbucks version?
For everyone who needs a fix, here’s my recipe from Nepal, with notes: https://t.co/GsT3nBhkjp https://t.co/Nyb1Iow04U
This post got a lot of attention. I wanted to add more, here's my advice of how to go from no experience in ML to getting hired (from someone who got a FAANG job as an ML scientist without any CS or ML degrees)
Classes/Resources to follow:
Getting hired for an intro level ML job only takes 2 graduate level classes in addition to a normal software engineering job, Intro to ML and Deep Learning with a Focus in your specialty area. See next tweet for links
Don’t skip the basics! Machine learning is extremely easy to do wrong. Here’s cautionary tale: Once I saw someone build an ML model, test it, and get 90%+ F1 score. They deployed the model to production, where it ran for months. They then transferred ownership of the model to my team, who investigated more deeply. It turns out the model was useless! It wasn’t doing any better than random guessing! The problem was there was train/test leakage, so the high test score was just from overfitting to examples in the test set. The people responsible for deploying the model originally didn’t know the importance of auditing + monitoring the model in production so they didn’t even know how useless it was. Knowing the basics would've prevented this.
Learn by building:
I can’t count how many times I’ve watched a lecture or read a paper, thought I understood it, but when I tried to implement it, I realized I didn’t understand as well as I thought I did.
Taking courses is important, but you’ll get so much more out of them if you start with a project idea in mind.
Don’t expect things to work perfectly first try. It usually takes me 50+ experiments before I have a model that I’m ready to release to production. Design your experiments so you can iterate quickly.
What projects to work on:
Start off with small projects, things you can imagine building in a weekend. As you learn more, you’ll be able to build bigger and cooler projects in the same amount of time (also you’ll build a good library of reusable code that’s common between the projects).
One good thing to practice is reading a paper and implementing it. That helps you understand the paper better, and can be really useful to other people.
For building a portfolio to get hired, it’s important that there’s some way to easily verify that the project is good. For example, if I tell you I trained a model and got 99% percent accuracy, that doesn’t really tell you anything, because maybe I just had some train/test leakage, or the data was imbalanced with more positives, or it was just an easy problem. Try to have some easily understandable impact from your model. For example, in my initial portfolio, I did one image generation project, and during the interview I showed the interviewer pictures from my phone. For another project I made a model to play the 2048 game. The model was able to get the 4096 tile, which is better than I could do myself. My last portfolio project was some freelance work I had done, so I could demonstrate business impact.
It’s even better if you show the quality of the project through a metric everyone recognizes. E.g. If you can do well in a kaggle competition, get a paper into a conference, or make a library or model that a lot of people use, then that will stand out.
Work with others:
Post about your work online. Share what you’ve learned. You’ll meet cool people and learn a lot from them. Getting attention from posting your work is a good way to make opportunities for yourself, especially compared to applying for jobs where you don’t even know if the recruiter will look at your resume.
There's a lot more to include (e.g. common pitfalls, how to think with an ML mindest), but wanted to start with this high level roadmap. If there's interest I can do a follow up post with more info about any common questions I get
Basically, I just want to make the point that all the information you need to get started in ML is freely available. You don't need fancy degrees or to be a genius. You just need to get the skills and build.
one of the more awesome things you can do with @duckdb
query geospatial data straight from github
this is from the excellent Natural Earth Data, and contains the boundaries for all countries in the world https://t.co/5QNjF2z4Rc