If you spend one year on applying deep learning, you can train a net on a 100 different data sets. That's where the intuition comes from. You'll debug a lot. People with zero experience with deep learning have ended in the top 10 for Kaggle computer vision competitions. Applied ML does not require a PhD in Computer Science. It is not like you are debugging Cuda as was the case a few years back. Even then, a PhD in statistics is unlikely to help with that. Really, one year of Python experience and you are good to go.
How rare is it to find a new problem domain these days? Most if not all problems tackled with deep learning have already been done in one form or the other. Deep learning is not yet much of an academic discipline, but accessible to anyone willing to put in the hours of study and practice.
How rare is it to find a new problem domain these days? Most if not all problems tackled with deep learning have already been done in one form or the other. Deep learning is not yet much of an academic discipline, but accessible to anyone willing to put in the hours of study and practice.