Linear Digressions

A podcast by Ben Jaffe and Katie Malone

Categorie:

289 Episodio

  1. Google Flu Trends

    Pubblicato: 26/3/2018
  2. How to pick projects for a professional data science team

    Pubblicato: 19/3/2018
  3. Autoencoders

    Pubblicato: 12/3/2018
  4. When Private Data Isn't Private Anymore

    Pubblicato: 5/3/2018
  5. What makes a machine learning algorithm "superhuman"?

    Pubblicato: 26/2/2018
  6. Open Data and Open Science

    Pubblicato: 19/2/2018
  7. Defining the quality of a machine learning production system

    Pubblicato: 12/2/2018
  8. Auto-generating websites with deep learning

    Pubblicato: 4/2/2018
  9. The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters

    Pubblicato: 29/1/2018
  10. The Case for Learned Index Structures, Part 1: B-Trees

    Pubblicato: 22/1/2018
  11. Challenges with Using Machine Learning to Classify Chest X-Rays

    Pubblicato: 15/1/2018
  12. The Fourier Transform

    Pubblicato: 8/1/2018
  13. Statistics of Beer

    Pubblicato: 2/1/2018
  14. Re - Release: Random Kanye

    Pubblicato: 24/12/2017
  15. Debiasing Word Embeddings

    Pubblicato: 18/12/2017
  16. The Kernel Trick and Support Vector Machines

    Pubblicato: 11/12/2017
  17. Maximal Margin Classifiers

    Pubblicato: 4/12/2017
  18. Re - Release: The Cocktail Party Problem

    Pubblicato: 27/11/2017
  19. Clustering with DBSCAN

    Pubblicato: 20/11/2017
  20. The Kaggle Survey on Data Science

    Pubblicato: 13/11/2017

7 / 15

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