Blog
#Machine Learning
“AI is the new electricity.” At least, that’s what Andrew Ng suggested at this year’s Amazon re:MARS conference. In his keynote address, Ng discussed th...
#Webinars
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 with existing workflows. I...
#Machine Learning
#Tutorials
To view the code, training visualizations, and more information about the python example at the end of this post, visit th...
#Academic Research
Authors: Ernesto Evgeniy Sanches Shayda (esanches@stanford.edu), Ilkyu Lee (lqlee@stanford.ed...
#Machine Learning
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 of approaches like bootstrapp...
#Machine Learning
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, and techniques to quantify u...
#Academic Research
Authors: Drake Johnson (drakej@stanford.edu), Tim Ngo (ngotm@stanford.edu), Augusto Fernandez (afyrxr@stanford.edu) I...
#Product
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 about: Challenges to be aware of when...
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