LT:: MAD Scientist – Lara Thompson:: ML/AI/Data Scientist

Studied physics, math, ~neuroscience; now do ML.

Boulder and run, read a lot, write some, always learning.

ML because it felt natural after physics, closer to what matters in the day to day, exciting in how powerful it can be, challenging in how badly it can fail; mixing math, programming, and problem solving to create products and services that help people rather than manipulate or merely distract, and build technology that means less waste and instead of yet more stuff.

I post stuff sporadically: notes for papers we’re reading at the Learn Data Science Meetup; data analysis of something fun; write up of concepts I worked through.

2024 Update

best laid plans…

Random list of updates and imminent plans.

Embeddings

They aren’t just for text

Some introductory notes on embeddings as pre-reading for our data science paper reading group.

How Bad is Top-K Recommendation under Competing Content Creators?

Introduction for Newbies and Summary for the Rushed

Backround notes on recommenders from a game theory / bandit learning perspective to help people understand this week’s paper.

Assess Yourself for Better Bouldering

Data-Backed Training Advice

Analysis of a climbing survey dataset to test the wisdom of the crowd in how best to improve at climbing and assess training progress.

Data is everywhere!

Uh, where again?

Last night I was on a data science career panel (of awesome ladies!) as part the Vancouver Datajam 2020 and I promised (as I’ve been meaning to do for a while…) to post a list of data resources.

GPU for ML only on Ubuntu

To go against the grain, you have to fight.

Site overhaul to get rid of Jekyll

…and get things working again!

If you know html+css, why use a framework? Turns out, a few reasons.

Linear Regression with Uncertainty

Everyone uses linear regression whether they admit it or not. Here I review the math behind it with estimates of uncertainty.

References for Data, ML, Math etc.

Some dated and more or less relevant references to get started in ML.

Learning to learn without forgetting: a summary

Bullet-point form summary of this excellent paper. Contributions include a memory store of past examples; balances learning of new updates with recall of these stored examples; continual (online) learning in supervised and reinforcement learning settings.