Towards Data Science

131 Odcinki

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

   Opublikowany: 16.09.2020
2. 50. Ken Jee - Building your brand in data science

   Opublikowany: 9.09.2020
3. 49. Catherine Zhou - The data science of learning

   Opublikowany: 2.09.2020
4. 48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

   Opublikowany: 26.08.2020
5. 47. Goku Mohandas - Industry research and how to show off your projects

   Opublikowany: 19.08.2020
6. 46. Ihab Ilyas - Data cleaning is finally being automated

   Opublikowany: 12.08.2020
7. 45. Kenny Ning - Is data science merging with data engineering?

   Opublikowany: 5.08.2020
8. 44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI

   Opublikowany: 29.07.2020
9. 43. Ian Scott - Data science at Deloitte

   Opublikowany: 22.07.2020
10. 42. Will Grathwohl - Energy-based models and the future of generative algorithms

    Opublikowany: 15.07.2020
11. 41. Solmaz Shahalizadeh - Data science in high-growth companies

    Opublikowany: 8.07.2020
12. 40. David Meza - Data science at NASA

    Opublikowany: 1.07.2020
13. 39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus

    Opublikowany: 24.06.2020
14. 38. Matthew Stewart - Data privacy and machine learning in environmental science

    Opublikowany: 17.06.2020
15. 37. Sean Knapp - The brave new world of data engineering

    Opublikowany: 10.06.2020
16. 36. Max Welling - The future of machine learning

    Opublikowany: 3.06.2020
17. 35. Rubén Harris - Learning and looking for jobs in quarantine

    Opublikowany: 27.05.2020
18. 34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why.

    Opublikowany: 20.05.2020
19. 33. Roland Memisevic - Machines that can see and hear

    Opublikowany: 13.05.2020
20. 32. Bahador Khalegi - Explainable AI and AI interpretability

    Opublikowany: 6.05.2020