Nelson Tang
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nelsontang.com
Nelson Tang
@nelsontang.com
Data Scientist (Forecasting) @NVIDIA. Interested in Bayesian stats, causal inference, and decisions. Also dad, OEF vet, and ski/mountain enjoyer.

I blog (throw bricks into the wind) @ www.nelsontang.com
Curious about this too, I don’t see much here but might be because of chronological feed or I’m just not following the right people
March 20, 2025 at 2:16 AM
That entry level job market is going to be sobering
March 17, 2025 at 3:15 AM
Couldn’t agree more - only poor options to be found here unfortunately. Hoping improvements are coming, but until then it’s a lot of long breaks away from the phone
February 19, 2025 at 6:07 AM
Have you considered the OnlyPosts feed?
February 19, 2025 at 4:53 AM
Yeah I think pyenv did that for me when I used it, it lets you pick a python version and set a global default (or manually activate a different one). If you haven’t nuked your computer yet, you can at least use ‘uv python list’ to see what’s installed and do some cleanups
February 9, 2025 at 11:20 PM
What are you trying to do? uv kind of assumes you want to use venv for stuff and know how to activate those or use an IDE that can detect it. If you want some global python install then you have stuff like conda and pyenv
February 9, 2025 at 10:31 PM
This whole time I’ve been learning Bayesian inference to detect biased coins and it turns out you can just look at it instead
February 6, 2025 at 1:37 PM
You can run R in quarto and source() what you need I think
February 6, 2025 at 2:22 AM
What book is this? The only other time I've seen the 'talent/skill tree' view is in Mathematics for Machine Learning (mml-book.github.io) and I wish we saw more of that view!
Mathematics for Machine Learning
Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press.
mml-book.github.io
February 6, 2025 at 12:01 AM
I guess I don't have a specific text I can share - you probably already have this, but the chapter in probml2 (Probabilistic ML - Advanced Topics) by Kevin Murphy that covers them as an intro to SSM is my other go-to probml.github.io/pml-book/boo...
probml.github.io
February 5, 2025 at 11:38 PM
And I like this paper, discusses HMM and state space models and Bayesian networks - mlg.eng.cam.ac.uk/zoubin/paper...
mlg.eng.cam.ac.uk
February 5, 2025 at 11:26 PM
Here’s an interactive intro: nipunbatra.github.io/hmm/
Exploring Hidden Markov Models
nipunbatra.github.io
February 5, 2025 at 11:23 PM
It’s important to budget time for yak shaving
February 4, 2025 at 3:35 PM
The quiet posters feed seems to be the best bet, otherwise everything just gets drowned out with people reacting to news
February 4, 2025 at 1:44 AM
Maybe an initial assignment could be around critiquing the LLM output based on something the student knows well or is an expert in. Like how you listen to certain podcast hosts and once you hear them talking about your field of expertise you realize they have no idea what they’re talking about
January 31, 2025 at 3:31 PM