Over the last 50 years, the rise/fall/rise in popularity of neural nets has acted as something of a 'barometer' for AI research.
It's clear from the questions on this site that people are interested in applying Deep Learning (DL) to a wide variety of difficult problems.
I therefore have two questions:
- Practitioners - What do you find to be the main obstacles to applying DL 'out of the box' to your problem?
- Researchers - What techniques do you use (or have developed) that might help address practical issues? Are they within DL or do they offer an alternative approach?