Lucas Javier Bernardi
where else to find them
no profile links on file
what they talk about
where they keep going back
- PyData Amsterdam4 editions2016–2019
on a stage with
never shared a talk
who talks about the same things
- Ruben Mak8 talks
- Michelle Frost7 talks
- Vincent D Warmerdam22 talks
- Eyal Kazin8 talks
- Aleksander Molak6 talks
- Danial Senejohnny3 talks
| title | conference | date |
|---|---|---|
| Casual Notes about Causal Stuff | PyData Amsterdam 2019 | 2019-05-10 |
Practical claims about extracting causal effects from Booking.com data: a reproducible pipeline with propensity scores and two‑step reweighting / upsampling, a… | ||
| Selection bias: The elephant in the room | PyData Amsterdam 2018 | 2018-05-25 |
Problem: training labels come only from later funnel stages so measured gains don’t reflect true production performance; experiments with a family‑trip… | ||
| Diagnosing Machine Learning Models | PyData Amsterdam 2017 | 2017-04-07 |
Linear OLS on 17,000 rows and ~40 inputs shows huge coefficient variance and many nonsignificant p values until one-hot encoding and redundant month/season… | ||
| Tools and Tricks from a Pragmatic Data Scientist | PyData Amsterdam 2016 | 2016-03-12 |
Claims concrete wins: cosine→Euclidean via normalization lets KD/ball‑trees return exact kNN results (no approximation) and empirical timings on MNIST and up… | ||
| nothing matches | ||