In this post, we’ll provide a short tutorial for training a RNN for speech recognition; we’re including code snippets throughout, and an accompanying GitHub repository. The software we’re using is a mix of borrowed and inspired code from existing open source projects.
Deploying a model without a rigorous process in place has consequences. We go over techniques for successful deployment and management.
In this post, we’ll be talking through a few tools that help make data science teams more productive.
This article reviews the main options for free speech recognition toolkits that use traditional HMM and n-gram language models.
In this post, we will explore some aspects of the train delay data we’ve been collecting from the Caltrain API.
One way to give back to the open source community that provides us with tools is to help others evaluate and choose those tools in a way that takes advantage of our experience. We offer this analysis, along with explanations of the various criteria upon which we based our decisions.
In this post, Matt talks about using TensorFlow to detect true and false positives in our Caltrain work.
Being data-driven means breaking down silos within organizations, promoting communication, and being deliberate about the data you collect and use. Here are five articles that illustrate how modern organizations are tackling this challenge.
A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn’t mean that one causes the other. This is a lesson worth learning.