[vonnik]

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...

[dragon_king]

Really 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]

It 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]

Very interesting. Thanks for the insight.

[Omnipresent]

Are 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]

A 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]

Thats interesting. Would you recommend any learning material for the fraud/anamoly detection using deep learning? Any available datasets one can play with?

[agibsonccc]

That'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-sec...

[RangerScience]

Can 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]

Both. 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]

Run 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]

A neural net that recognizes sentiment of facial expressions based on camera footage.