Data Science at Home

254 Episodo

1. Is Rust flexible enough for a flexible data model? (Ep. 137)

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

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

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

   Publicado: 31/12/2020
5. What is data ethics? (Ep. 133)

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

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

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

   Publicado: 1/12/2020
9. Similarity in Machine Learning (Ep. 129)

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

    Publicado: 17/11/2020
11. Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127)

    Publicado: 11/11/2020
12. Top-3 ways to put machine learning models into production (Ep. 126)

    Publicado: 7/11/2020
13. Remove noise from data with deep learning (Ep.125)

    Publicado: 3/11/2020
14. What is contrastive learning and why it is so powerful? (Ep. 124)

    Publicado: 30/10/2020
15. Neural search (Ep. 123)

    Publicado: 23/10/2020
16. Let's talk about federated learning (Ep. 122)

    Publicado: 18/10/2020
17. How to test machine learning in production (Ep. 121)

    Publicado: 11/10/2020
18. Why synthetic data cannot boost machine learning (Ep. 120)

    Publicado: 26/9/2020
19. Machine learning in production: best practices [LIVE from twitch.tv]

    Publicado: 16/9/2020
20. Testing in machine learning: checking deeplearning models (Ep. 118)

    Publicado: 4/9/2020