~/talks

Lorena Mesa

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Diversity & Inclusion WorkgroupPyCon US 20222022-04-27
Started November 2020 with 20 members, the workgroup documents concrete election problems: 2020 had four board seats and 26 nominees yet all winners are from…
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Lightning Talks 1PyCon US 20212021-05-14
Fast specifics: Friendly improves tracebacks with "where/why/what" queries, Jupyter support and French translations; tox 4 is three times faster and adds wheel…
Lightning on
Lightning Talks 2PyCon US 20212021-05-14
Short lightning slate: concrete claims and tools, not platitudes. Expect Moulding Data for ML: three cycles and bias corrections, more data ≠ better data, a…
Lightning on
9 Years of PyLadies: Lessons Learned and What Comes NextPyCon US 20202020-04-15
Nine years of growth claims move from seven founders to 100+ chapters worldwide but also surface concrete problems: volunteer burnout, stagnant resources, and…
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Keynote: The Python Butterfly EffectPyCon Latam 20192019-08-29
A keynote shows how small choices in code and community produce large social effects, demonstrating a Jupyter Notebook simulation of chaos and interest…
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Now is better than Never: What the Zen of Python can teach us about Data EthicsPyOhio 20182018-07-28
Argues that the Zen line Now is better than never means start fixing data harms now, not later, and shows concrete failures: automated moderation funnels…
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using-django-docker-and-scikit-learn-to-bootstrap-your-machine-learning-project/DjangoCon US 20172017-08-13
Claims that Django can spin up Docker containers that build and version models, using Docker images as reproducible snapshots containing code data and a…
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Is that spam in my ham? A novice's inquiry into classification.PyOhio 20152015-08-01
Defines spam detection as a binary classification, trains a Naive Bayes model on 2,500 labeled emails (1,721 ham, 779 spam), tokenizes bodies into a…
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