Data Science at Home

268 Épisodes

1. What happens to data transfer after Schrems II? (Ep. 131)

   Publié: 04/12/2020
2. Test-First Machine Learning [RB] (Ep. 130)

   Publié: 01/12/2020
3. Similarity in Machine Learning (Ep. 129)

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

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

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

   Publié: 07/11/2020
7. Remove noise from data with deep learning (Ep.125)

   Publié: 03/11/2020
8. What is contrastive learning and why it is so powerful? (Ep. 124)

   Publié: 30/10/2020
9. Neural search (Ep. 123)

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

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

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

    Publié: 26/09/2020
13. Machine learning in production: best practices [LIVE from twitch.tv]

    Publié: 16/09/2020
14. Testing in machine learning: checking deeplearning models (Ep. 118)

    Publié: 04/09/2020
15. Testing in machine learning: generating tests and data (Ep. 117)

    Publié: 29/08/2020
16. Why you care about homomorphic encryption (Ep. 116)

    Publié: 12/08/2020
17. Test-First machine learning (Ep. 115)

    Publié: 03/08/2020
18. GPT-3 cannot code (and never will) (Ep. 114)

    Publié: 26/07/2020
19. Make Stochastic Gradient Descent Fast Again (Ep. 113)

    Publié: 22/07/2020
20. What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)

    Publié: 19/07/2020