Gradient Dissent: Conversations on AI
Podcast autorstwa Lukas Biewald
120 Odcinki
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Polly Fordyce — Microfluidic Platforms and Machine Learning
Opublikowany: 29.04.2021 -
Adrien Gaidon — Advancing ML Research in Autonomous Vehicles
Opublikowany: 22.04.2021 -
Nimrod Shabtay — Deployment and Monitoring at Nanit
Opublikowany: 15.04.2021 -
Chris Mattmann — ML Applications on Earth, Mars, and Beyond
Opublikowany: 8.04.2021 -
Vladlen Koltun — The Power of Simulation and Abstraction
Opublikowany: 1.04.2021 -
Dominik Moritz — Building Intuitive Data Visualization Tools
Opublikowany: 25.03.2021 -
Cade Metz — The Stories Behind the Rise of AI
Opublikowany: 18.03.2021 -
Dave Selinger — AI and the Next Generation of Security Systems
Opublikowany: 11.03.2021 -
Tim & Heinrich — Democraticizing Reinforcement Learning Research
Opublikowany: 4.03.2021 -
Daphne Koller — Digital Biology and the Next Epoch of Science
Opublikowany: 18.02.2021 -
Piero Molino — The Secret Behind Building Successful Open Source Projects
Opublikowany: 11.02.2021 -
Rosanne Liu — Conducting Fundamental ML Research as a Nonprofit
Opublikowany: 5.02.2021 -
Sean Gourley — NLP, National Defense, and Establishing Ground Truth
Opublikowany: 28.01.2021 -
Peter Wang — Anaconda, Python, and Scientific Computing
Opublikowany: 22.01.2021 -
Chris Anderson — Robocars, Drones, and WIRED Magazine
Opublikowany: 14.01.2021 -
Adrien Treuille — Building Blazingly Fast Tools That People Love
Opublikowany: 4.12.2020 -
Peter Norvig – Singularity Is in the Eye of the Beholder
Opublikowany: 20.11.2020 -
Robert Nishihara — The State of Distributed Computing in ML
Opublikowany: 13.11.2020 -
Ines & Sofie — Building Industrial-Strength NLP Pipelines
Opublikowany: 29.10.2020 -
Daeil Kim — The Unreasonable Effectiveness of Synthetic Data
Opublikowany: 16.10.2020
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.
