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Typical use cases are: * fraud and anomaly detection * recommender systems * predictive analytics (churn, forecasting) * image recognition With image recog
by vonnik 10y ago
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-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-featured-in-wall-street-journal/ http://www.orangesv.com/blog/orange-deep-learning-work-featu...
- dragon_king 10y agoReally cool and interesting. Looks like deep learning and AI is going to make some significant strides in the tech industry. I read somewhere that there is still a lack of professionals to expedite the adoption in the wild. What additional skills are required by your regular full stack software engineer to be useful for a deep learning company? I know there are courses out there but I am not sure if they are too academic. Are there specific technologies that one should be playing with?
- agibsonccc 10y agoIt varies. Sometimes it's just understanding enough about data to build data products. (Eg: a website with a recommendation engine component) Knowing data visualization can also be useful. Most deep learning companies focus on a particular application. FWIW I'm actually self taught. I did client projects and learned machine learning on my own. You could start by branching in to data engineering and understanding how data pipelines work. That's closer to the skills a full stack developer is likely to have.
- dragon_king 10y agoVery interesting. Thanks for the insight.
- RangerScience 10y agoCan you go into more detail on the image recognition? Do you mean AR-style image recognition ("find exactly this image"), or more image /classification/ (here's what's in this image)?
- agibsonccc 10y agoBoth. Given an example: Sentiment analysis on a car show floor detecting sentiment of buyers. It can also just be binary: "Is this object present or not?"
- weavie 10y agoRun that by me again.. So you have an algorithm that attaches to a camera in a car shop and can determine the likelihood that a buyer is interested in the car? That's pretty awesome and scary in equal measure..
- agibsonccc 10y agoA neural net that recognizes sentiment of facial expressions based on camera footage.
- Omnipresent 10y agoAre there some documented and testable examples of fraud and anomaly detection via deep learning? I'm asking from being able to play around / learn more about this domain.
- agibsonccc 10y agoA lot of it is actually our customers. We've been covering that kind of R&D ourselves. That's the fun of being able to look at applications outside of the google,fb hype.
- Omnipresent 10y agoThats interesting. Would you recommend any learning material for the fraud/anamoly detection using deep learning? Any available datasets one can play with?
- agibsonccc 10y agoThat's actually a big problem. Very few datasets out there, because the data tends to be sensitive. Here's one we use for demos: https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/ https://www.unsw.adfa.edu.au/australian-centre-for-cyber-sec...