Linear Digressions

289 Avsnitt

1. Network effects re-release: when the power of a public health measure lies in widespread adoption

   Publicerades: 2020-03-15
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Publicerades: 2020-03-09
3. Better know a distribution: the Poisson distribution

   Publicerades: 2020-03-02
4. The Lottery Ticket Hypothesis

   Publicerades: 2020-02-23
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Publicerades: 2020-02-17
6. Thinking of data science initiatives as innovation initiatives

   Publicerades: 2020-02-10
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Publicerades: 2020-02-02
8. Running experiments when there are network effects

   Publicerades: 2020-01-27
9. Zeroing in on what makes adversarial examples possible

   Publicerades: 2020-01-20
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Publicerades: 2020-01-13
11. Data scientists: beware of simple metrics

    Publicerades: 2020-01-05
12. Communicating data science, from academia to industry

    Publicerades: 2019-12-30
13. Optimizing for the short-term vs. the long-term

    Publicerades: 2019-12-23
14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Publicerades: 2019-12-16
15. Using machine learning to predict drug approvals

    Publicerades: 2019-12-08
16. Facial recognition, society, and the law

    Publicerades: 2019-12-02
17. Lessons learned from doing data science, at scale, in industry

    Publicerades: 2019-11-25
18. Varsity A/B Testing

    Publicerades: 2019-11-18
19. The Care and Feeding of Data Scientists: Growing Careers

    Publicerades: 2019-11-11
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Publicerades: 2019-11-04