How to Make Remote Work Effective for Data Science Teams
This article was written in collaboration with Tyler Folkman, Head of AI at Branded Entertainment Network. To read more of…
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…
Stanford Research Series: Making Trading Great Again: Trump-based Stock Predictions via doc2vec Embeddings
Authors: Rifath Rashid (rifath@stanford.edu) and Anton de Leon (aadeleon@atanford.edu) 1 Introduction The question of how accurately Twitter posts can model…
Estimating Uncertainty in Machine Learning Models – Part 2
You can check out part 1 of this series here In part 1 of this series, we discussed the sources…
