Comet Industry Q&A (Recap): Collaborative Data Science and Machine Learning
A conversation with Abubakar Abid of Gradio and Jakub Jurovych of Deepnote.
Upcoming Comet Industry Q&A: Collaborative Data Science and Machine Learning
Join us Monday, July 26th, for an Industry Q&A exploring the nature and evolution of collaborative data science and machine learning.
comet.ml announces $4.5 million investment to double-down on Comet Enterprise and Meta Learning Capabilities
Current customers include Ancestry, General Electric, The National Institute of Health and Fortune 100 Companies NEW YORK, NY--comet.ml, the industry-leading…
Why software engineering processes and tools don’t work for machine learning
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.
How to apply machine learning and deep learning methods to audio analysis
To view the code, training visualizations, and more information about the python example at the end of this post, visit…
Stanford Research Series: Grand Digital Piano: Multimodal Transfer of Learning of Sound and Touch
Authors: Ernesto Evgeniy Sanches Shayda (esanches@stanford.edu), Ilkyu Lee (lqlee@stanford.edu) I. MOTIVATION Musical instruments have evolved during thousands of years allowing…
Estimating Uncertainty in Machine Learning Models — Part 3
Check out part 1 (here)and part 2 (here) of this series In the last part of our series on…
