Gradient Dissent: Conversations on AI

107 Episodo

1. Peter Wang — Anaconda, Python, and Scientific Computing

   Publicado: 21/1/2021
2. Chris Anderson — Robocars, Drones, and WIRED Magazine

   Publicado: 14/1/2021
3. Adrien Treuille — Building Blazingly Fast Tools That People Love

   Publicado: 4/12/2020
4. Peter Norvig – Singularity Is in the Eye of the Beholder

   Publicado: 20/11/2020
5. Robert Nishihara — The State of Distributed Computing in ML

   Publicado: 13/11/2020
6. Ines & Sofie — Building Industrial-Strength NLP Pipelines

   Publicado: 29/10/2020
7. Daeil Kim — The Unreasonable Effectiveness of Synthetic Data

   Publicado: 15/10/2020
8. Joaquin Candela — Definitions of Fairness

   Publicado: 1/10/2020
9. Richard Socher — The Challenges of Making ML Work in the Real World

   Publicado: 29/9/2020
10. Zack Chase Lipton — The Medical Machine Learning Landscape

    Publicado: 17/9/2020
11. Anthony Goldbloom — How to Win Kaggle Competitions

    Publicado: 9/9/2020
12. Suzana Ilić — Cultivating Machine Learning Communities

    Publicado: 2/9/2020
13. Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

    Publicado: 25/8/2020
14. Anantha Kancherla — Building Level 5 Autonomous Vehicles

    Publicado: 12/8/2020
15. Bharath Ramsundar — Deep Learning for Molecules and Medicine Discovery

    Publicado: 5/8/2020
16. Chip Huyen — ML Research and Production Pipelines

    Publicado: 29/7/2020
17. Peter Skomoroch — Product Management for AI

    Publicado: 21/7/2020
18. Josh Tobin — Productionizing ML Models

    Publicado: 8/7/2020
19. Miles Brundage — Societal Impacts of Artificial Intelligence

    Publicado: 1/7/2020
20. Hamel Husain — Building Machine Learning Tools

    Publicado: 24/6/2020