Towards Data Science

131 Avsnitt

1. 51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need

   Publicerades: 2020-09-16
2. 50. Ken Jee - Building your brand in data science

   Publicerades: 2020-09-09
3. 49. Catherine Zhou - The data science of learning

   Publicerades: 2020-09-02
4. 48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

   Publicerades: 2020-08-26
5. 47. Goku Mohandas - Industry research and how to show off your projects

   Publicerades: 2020-08-19
6. 46. Ihab Ilyas - Data cleaning is finally being automated

   Publicerades: 2020-08-12
7. 45. Kenny Ning - Is data science merging with data engineering?

   Publicerades: 2020-08-05
8. 44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI

   Publicerades: 2020-07-29
9. 43. Ian Scott - Data science at Deloitte

   Publicerades: 2020-07-22
10. 42. Will Grathwohl - Energy-based models and the future of generative algorithms

    Publicerades: 2020-07-15
11. 41. Solmaz Shahalizadeh - Data science in high-growth companies

    Publicerades: 2020-07-08
12. 40. David Meza - Data science at NASA

    Publicerades: 2020-07-01
13. 39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus

    Publicerades: 2020-06-24
14. 38. Matthew Stewart - Data privacy and machine learning in environmental science

    Publicerades: 2020-06-17
15. 37. Sean Knapp - The brave new world of data engineering

    Publicerades: 2020-06-10
16. 36. Max Welling - The future of machine learning

    Publicerades: 2020-06-03
17. 35. Rubén Harris - Learning and looking for jobs in quarantine

    Publicerades: 2020-05-27
18. 34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why.

    Publicerades: 2020-05-20
19. 33. Roland Memisevic - Machines that can see and hear

    Publicerades: 2020-05-13
20. 32. Bahador Khalegi - Explainable AI and AI interpretability

    Publicerades: 2020-05-06