Linear Digressions

289 Episodio

1. Interview with Joel Grus

   Pubblicato: 10/6/2019
2. Re - Release: Factorization Machines

   Pubblicato: 3/6/2019
3. Re-release: Auto-generating websites with deep learning

   Pubblicato: 27/5/2019
4. Advice to those trying to get a first job in data science

   Pubblicato: 19/5/2019
5. Re - Release: Machine Learning Technical Debt

   Pubblicato: 12/5/2019
6. Estimating Software Projects, and Why It's Hard

   Pubblicato: 5/5/2019
7. The Black Hole Algorithm

   Pubblicato: 29/4/2019
8. Structure in AI

   Pubblicato: 21/4/2019
9. The Great Data Science Specialist vs. Generalist Debate

   Pubblicato: 15/4/2019
10. Google X, and Taking Risks the Smart Way

    Pubblicato: 8/4/2019
11. Statistical Significance in Hypothesis Testing

    Pubblicato: 1/4/2019
12. The Language Model Too Dangerous to Release

    Pubblicato: 25/3/2019
13. The cathedral and the bazaar

    Pubblicato: 17/3/2019
14. AlphaStar

    Pubblicato: 11/3/2019
15. Are machine learning engineers the new data scientists?

    Pubblicato: 4/3/2019
16. Interview with Alex Radovic, particle physicist turned machine learning researcher

    Pubblicato: 25/2/2019
17. K Nearest Neighbors

    Pubblicato: 17/2/2019
18. Not every deep learning paper is great. Is that a problem?

    Pubblicato: 11/2/2019
19. The Assumptions of Ordinary Least Squares

    Pubblicato: 3/2/2019
20. Quantile Regression

    Pubblicato: 28/1/2019