From the Twitter Engineering Blog
- Distributed learning in Torch Monday, January 25, 2016
We recently released Autograd for Torch, which greatly simplified our workflow when experimenting with complex deep learning architectures. The Twitter Cortex team is continuously investing in better tooling for manipulating our large datasets, and distributing training processes across machines in our cluster.
Today we’re open-sourcing four components of our training pipeline, so the community using Torch and/or Autograd can simplify their workflows when it comes to parallelizing training, and manipulating large, distributed datasets.
- Implications of use of multiple controls in an A/B test Wednesday, January 13, 2016
Using a second control can be a tempting method of validating experiment results. We explore the statistics underlying usage of a second control, and conclude that this approach is strictly inferior to using a single large control.
- Visually explore funnels of user activities Wednesday, January 06, 2016
We describe our experimental visual analytics approach for funnel analysis, which helps us explore how users interact with the user interfaces and gain new insights for improving user engagement with Twitter.
- Detecting and avoiding bucket imbalance in A/B tests Monday, December 28, 2015
Some simple techniques to detect potentially biased implementations of A/B tests.