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.
Running machine learning initiatives is difficult. Why? It is not possible for data scientists and teams to manage reproducibility, loss of IP, visibility and tracking…
To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. Introduction…
Authors: Ernesto Evgeniy Sanches Shayda (esanches@stanford.edu), Ilkyu Lee (lqlee@stanford.edu) I. MOTIVATION Musical instruments have evolved during thousands of years allowing performers to produce almost all…
Check out part 1 (here)and part 2 (here) of this series In the last part of our series on uncertainty estimation, we addressed the limitations…
Authors: Rifath Rashid (rifath@stanford.edu) and Anton de Leon (aadeleon@atanford.edu) 1 Introduction The question of how accurately Twitter posts can model the movement of financial securities…
You can check out part 1 of this series here In part 1 of this series, we discussed the sources of uncertainty in machine learning models,…
Authors: Drake Johnson (drakej@stanford.edu), Tim Ngo (ngotm@stanford.edu), Augusto Fernandez (afyrxr@stanford.edu) I. Introduction: Recently, there has been an increase in interest in the public for the…
Hosted by: Comet.ml and Pearl Cohen This week, we hosted a webinar with the patent experts at Pearl Cohen. During the webinar, Pearl Cohen talks…
Authors: Danny Takeuchi (dtakeuch@stanford.edu), Raymond Thai (raythai@stanford.edu), Kevin Tran (ktran23@stanford.edu) This project tackles several current issues with automated chest X-ray radiography, specifically regarding work on…