Linear Digressions

289 Episodio

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

   Pubblicato: 15/3/2020
2. Causal inference when you can't experiment: difference-in-differences and synthetic controls

   Pubblicato: 9/3/2020
3. Better know a distribution: the Poisson distribution

   Pubblicato: 2/3/2020
4. The Lottery Ticket Hypothesis

   Pubblicato: 23/2/2020
5. Interesting technical issues prompted by GDPR and data privacy concerns

   Pubblicato: 17/2/2020
6. Thinking of data science initiatives as innovation initiatives

   Pubblicato: 10/2/2020
7. Building a curriculum for educating data scientists: Interview with Prof. Xiao-Li Meng

   Pubblicato: 2/2/2020
8. Running experiments when there are network effects

   Pubblicato: 27/1/2020
9. Zeroing in on what makes adversarial examples possible

   Pubblicato: 20/1/2020
10. Unsupervised Dimensionality Reduction: UMAP vs t-SNE

    Pubblicato: 13/1/2020
11. Data scientists: beware of simple metrics

    Pubblicato: 5/1/2020
12. Communicating data science, from academia to industry

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

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

    Pubblicato: 16/12/2019
15. Using machine learning to predict drug approvals

    Pubblicato: 8/12/2019
16. Facial recognition, society, and the law

    Pubblicato: 2/12/2019
17. Lessons learned from doing data science, at scale, in industry

    Pubblicato: 25/11/2019
18. Varsity A/B Testing

    Pubblicato: 18/11/2019
19. The Care and Feeding of Data Scientists: Growing Careers

    Pubblicato: 11/11/2019
20. The Care and Feeding of Data Scientists: Recruiting and Hiring Data Scientists

    Pubblicato: 4/11/2019