Data Science at Home

264 Episodes

1. Learning and training in AI times (Ep. 148)

   Published: 4/13/2021
2. You are the product [RB] (Ep. 147)

   Published: 4/11/2021
3. Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)

   Published: 4/8/2021
4. Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)

   Published: 3/26/2021
5. Pandas vs Rust (Ep. 144)

   Published: 3/19/2021
6. Concurrent is not parallel - Part 2 (Ep. 143)

   Published: 3/13/2021
7. Concurrent is not parallel - Part 1 (Ep. 142)

   Published: 3/10/2021
8. Backend technologies for machine learning in production (Ep. 141)

   Published: 3/2/2021
9. How to reinvent banking and finance with data and technology (Ep. 139)

   Published: 2/15/2021
10. What's up with WhatsApp? (Ep. 138)

    Published: 2/7/2021
11. Is Rust flexible enough for a flexible data model? (Ep. 137)

    Published: 2/1/2021
12. Is Apple M1 good for machine learning? (Ep.136)

    Published: 1/25/2021
13. Rust and deep learning with Daniel McKenna (Ep. 135)

    Published: 1/18/2021
14. Scaling machine learning with clusters and GPUs (Ep. 134)

    Published: 12/31/2020
15. What is data ethics? (Ep. 133)

    Published: 12/19/2020
16. A Standard for the Python Array API (Ep. 132)

    Published: 12/8/2020
17. What happens to data transfer after Schrems II? (Ep. 131)

    Published: 12/4/2020
18. Test-First Machine Learning [RB] (Ep. 130)

    Published: 12/1/2020
19. Similarity in Machine Learning (Ep. 129)

    Published: 11/24/2020
20. Distill data and train faster, better, cheaper (Ep. 128)

    Published: 11/17/2020