Linear Digressions

289 Odcinki

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

   Opublikowany: 15.03.2020
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Opublikowany: 9.03.2020
3. Better know a distribution: the Poisson distribution

   Opublikowany: 2.03.2020
4. The Lottery Ticket Hypothesis

   Opublikowany: 23.02.2020
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Opublikowany: 17.02.2020
6. Thinking of data science initiatives as innovation initiatives

   Opublikowany: 10.02.2020
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Opublikowany: 2.02.2020
8. Running experiments when there are network effects

   Opublikowany: 27.01.2020
9. Zeroing in on what makes adversarial examples possible

   Opublikowany: 20.01.2020
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Opublikowany: 13.01.2020
11. Data scientists: beware of simple metrics

    Opublikowany: 5.01.2020
12. Communicating data science, from academia to industry

    Opublikowany: 30.12.2019
13. Optimizing for the short-term vs. the long-term

    Opublikowany: 23.12.2019
14. Interview with Prof. Andrew Lo, on using data science to inform complex business decisions

    Opublikowany: 16.12.2019
15. Using machine learning to predict drug approvals

    Opublikowany: 8.12.2019
16. Facial recognition, society, and the law

    Opublikowany: 2.12.2019
17. Lessons learned from doing data science, at scale, in industry

    Opublikowany: 25.11.2019
18. Varsity A/B Testing

    Opublikowany: 18.11.2019
19. The Care and Feeding of Data Scientists: Growing Careers

    Opublikowany: 11.11.2019
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Opublikowany: 4.11.2019