Gradient Dissent: Conversations on AI

En podcast af Lukas Biewald

Kategorier:

120 Episoder

  1. Joaquin Candela — Definitions of Fairness

    Udgivet: 1.10.2020
  2. Richard Socher — The Challenges of Making ML Work in the Real World

    Udgivet: 29.9.2020
  3. Zack Chase Lipton — The Medical Machine Learning Landscape

    Udgivet: 17.9.2020
  4. Anthony Goldbloom — How to Win Kaggle Competitions

    Udgivet: 9.9.2020
  5. Suzana Ilić — Cultivating Machine Learning Communities

    Udgivet: 2.9.2020
  6. Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML

    Udgivet: 25.8.2020
  7. Anantha Kancherla — Building Level 5 Autonomous Vehicles

    Udgivet: 12.8.2020
  8. Bharath Ramsundar — Deep Learning for Molecules and Medicine Discovery

    Udgivet: 5.8.2020
  9. Chip Huyen — ML Research and Production Pipelines

    Udgivet: 29.7.2020
  10. Peter Skomoroch — Product Management for AI

    Udgivet: 22.7.2020
  11. Josh Tobin — Productionizing ML Models

    Udgivet: 8.7.2020
  12. Miles Brundage — Societal Impacts of Artificial Intelligence

    Udgivet: 1.7.2020
  13. Hamel Husain — Building Machine Learning Tools

    Udgivet: 24.6.2020
  14. Peter Welinder — Deep Reinforcement Learning and Robotics

    Udgivet: 17.6.2020
  15. Vicki Boykis — Machine Learning Across Industries

    Udgivet: 4.6.2020
  16. Angela & Danielle — Designing ML Models for Millions of Consumer Robots

    Udgivet: 6.5.2020
  17. Jack Clark — Building Trustworthy AI Systems

    Udgivet: 22.4.2020
  18. Rachael Tatman — Conversational AI and Linguistics

    Udgivet: 7.4.2020
  19. Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars

    Udgivet: 21.3.2020
  20. Brandon Rohrer — Machine Learning in Production for Robots

    Udgivet: 11.3.2020

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Join Lukas Biewald on Gradient Dissent, an AI-focused podcast brought to you by Weights & Biases. Dive into fascinating conversations with industry giants from NVIDIA, Meta, Google, Lyft, OpenAI, and more. Explore the cutting-edge of AI and learn the intricacies of bringing models into production.

Visit the podcast's native language site