Towards Data Science

131 Episodo

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

   Publicado: 16/9/2020
2. 50. Ken Jee - Building your brand in data science

   Publicado: 9/9/2020
3. 49. Catherine Zhou - The data science of learning

   Publicado: 2/9/2020
4. 48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products

   Publicado: 26/8/2020
5. 47. Goku Mohandas - Industry research and how to show off your projects

   Publicado: 19/8/2020
6. 46. Ihab Ilyas - Data cleaning is finally being automated

   Publicado: 12/8/2020
7. 45. Kenny Ning - Is data science merging with data engineering?

   Publicado: 5/8/2020
8. 44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI

   Publicado: 29/7/2020
9. 43. Ian Scott - Data science at Deloitte

   Publicado: 22/7/2020
10. 42. Will Grathwohl - Energy-based models and the future of generative algorithms

    Publicado: 15/7/2020
11. 41. Solmaz Shahalizadeh - Data science in high-growth companies

    Publicado: 8/7/2020
12. 40. David Meza - Data science at NASA

    Publicado: 1/7/2020
13. 39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus

    Publicado: 24/6/2020
14. 38. Matthew Stewart - Data privacy and machine learning in environmental science

    Publicado: 17/6/2020
15. 37. Sean Knapp - The brave new world of data engineering

    Publicado: 10/6/2020
16. 36. Max Welling - The future of machine learning

    Publicado: 3/6/2020
17. 35. Rubén Harris - Learning and looking for jobs in quarantine

    Publicado: 27/5/2020
18. 34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why.

    Publicado: 20/5/2020
19. 33. Roland Memisevic - Machines that can see and hear

    Publicado: 13/5/2020
20. 32. Bahador Khalegi - Explainable AI and AI interpretability

    Publicado: 6/5/2020