Linear Digressions

A podcast by Ben Jaffe and Katie Malone

Categorie:

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

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In each episode, your hosts explore machine learning and data science through interesting (and often very unusual) applications.

Visit the podcast's native language site