Typical use cases are:
* fraud and anomaly detection
* recommender systems
* predictive analytics (churn, forecasting)
* image recognition
With image recognition, we hit 98% accuracy on a recent project. Until a few years ago, that was unheard of, and it's simply not possible with other algorithms, so for many companies, deep neural nets can make a significant difference.
Here are two news stories about work we've done for clients:
Making deep learning accessible on Openstack
https://insights.ubuntu.com/2016/04/25/making-deep-learning-...
For Canonical, we built a solution that predicts server breakdowns.
For France Telecom's mobile unit, Orange, we built a fraud detection solution using anomaly detection:
http://www.orangesv.com/blog/orange-deep-learning-work-featu...
* fraud and anomaly detection
* recommender systems
* predictive analytics (churn, forecasting)
* image recognition
With image recognition, we hit 98% accuracy on a recent project. Until a few years ago, that was unheard of, and it's simply not possible with other algorithms, so for many companies, deep neural nets can make a significant difference.
Here are two news stories about work we've done for clients:
Making deep learning accessible on Openstack https://insights.ubuntu.com/2016/04/25/making-deep-learning-...
For Canonical, we built a solution that predicts server breakdowns.
For France Telecom's mobile unit, Orange, we built a fraud detection solution using anomaly detection:
http://www.orangesv.com/blog/orange-deep-learning-work-featu...