The Twitter Engineering Blog

Information from Twitter's engineering team about our technology, tools and events.

Results from Engineering for: January 2016

Distributed learning in Torch

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.

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Implications of use of multiple controls in an A/B test

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.

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Visually explore funnels of user activities

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.

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