Data Science at Home

264 Episodo

1. Episode 24: How to handle imbalanced datasets

   Publicado: 9/10/2017
2. Episode 23: Why do ensemble methods work?

   Publicado: 3/10/2017
3. Episode 22: Parallelising and distributing Deep Learning

   Publicado: 25/9/2017
4. Episode 21: Additional optimisation strategies for deep learning

   Publicado: 18/9/2017
5. Episode 20: How to master optimisation in deep learning

   Publicado: 28/8/2017
6. Episode 19: How to completely change your data analytics strategy with deep learning

   Publicado: 9/8/2017
7. Episode 18: Machines that learn like humans

   Publicado: 28/3/2017
8. Episode 17: Protecting privacy and confidentiality in data and communications

   Publicado: 15/2/2017
9. Episode 16: 2017 Predictions in Data Science

   Publicado: 23/12/2016
10. Episode 15: Statistical analysis of phenomena that smell like chaos

    Publicado: 5/12/2016
11. Episode 14: The minimum required by a data scientist

    Publicado: 27/9/2016
12. Episode 13: Data Science and Fraud Detection at iZettle

    Publicado: 6/9/2016
13. Episode 12: EU Regulations and the rise of Data Hijackers

    Publicado: 26/7/2016
14. Episode 11: Representative Subsets For Big Data Learning

    Publicado: 3/5/2016
15. Episode 10: History and applications of Deep Learning

    Publicado: 14/3/2016
16. Episode 9: Markov Chain Montecarlo with full conditionals

    Publicado: 2/3/2016
17. Episode 8: Frequentists and Bayesians

    Publicado: 15/2/2016
18. Episode 7: 30 min with data scientist Sebastian Raschka

    Publicado: 15/2/2016
19. Episode 6: How to be data scientist

    Publicado: 19/1/2016
20. Episode 5: Development and Testing Practices in Data Science

    Publicado: 13/1/2016