Banana Data Podcast

59 Folgen

1. BDN #19: How We Talk about AI, feat. Karen Hao, MIT Technology Review

   Vom: 28.2.2020
2. BDN #18: The Everyday of AI

   Vom: 14.2.2020
3. BDN #18: The Future (and the now) of AI

   Vom: 1.2.2020
4. BDN #16: How do you define AI?

   Vom: 17.1.2020
5. BDN #15: Finding Community in Data Science with Reshama Shaikh, key scikit-Learn sprint organizer

   Vom: 3.1.2020
6. Why Open Source? feat. Andreas Mueller, Core Contributor of scikit-Learn

   Vom: 20.12.2019
7. BDN #13: Predicting AI Trends for 2020

   Vom: 7.12.2019
8. BDN #12: Life after Production, a Tale of Technical Debt with Dan Shiebler, Twitter Eng

   Vom: 15.11.2019
9. BDN #11: The Essentials (and not-so essentials) of Data Science Pipelines

   Vom: 1.11.2019
10. BDN #10: What Makes a Good Data Science Practice

    Vom: 13.9.2019
11. BDN #9: The Death of Data Viz, Cross-Cultural AI, and AI Auditing

    Vom: 30.8.2019
12. BDN #8: Prioritizing training data, model interpretability, and dodging an AI Winter

    Vom: 16.8.2019
13. BDN #7: Building accessible queries, codes, and speech using AI

    Vom: 2.8.2019
14. BDN #6: AI Meets World: GDPR, the AI Job Apocalypse, and AI’s carbon footprint

    Vom: 19.7.2019
15. BDN #5: A New Kind of Relationship with AI: Robopets, AI Art, and AI EQ

    Vom: 5.7.2019
16. BDN #4: The future of data according to predictions, Python 3.0, and people.

    Vom: 22.6.2019
17. BDN #3: Culpability in AI failures, Fooling NNs with NNs, AI for cancer screenings, and Epsilon Greedy Multi-Armed Bandits

    Vom: 7.6.2019
18. BDN #2: Biased Data & the Perfect Answer, Multi-Armed Bandits, and the GPUs Behind Your Neural Networks

    Vom: 23.5.2019
19. BDN #1: Being an Ethical Data Scientist, Federated Learning in Healthcare, and Dropping the “Best Model” Approach

    Vom: 9.5.2019