Here's another way to see: What have you been doing for past two years? Now imagine you had started learning this about 2 years ago. You would have been done by now and ready to tackle some of the most interesting problems instead of continue to do same boring stuff you had been doing for past 2 years. In 2016, come back here and look at this comment again :).
PS: For people who are saying you can "apply" DNNs in a day or learn it by a coursera course in 6 weeks - they are only very superficially right. Yeah, anyone can build ML model for a sample training data using tool in the same sense that anyone can compile sample code and have a working app. The problem is that most models don't work the first time as expected. The challenge lies in debugging the model and fix many of N possibilities to make it work. This is what working in ML is all about. It's like usual programming where it takes years of experience to debug the code and make it work for your purpose. The added twist in ML is that debugging is almost entirely statistical. When your model doesn't work, it doesn't work only in statistical sense. Your problem would be essentially that the model doesn't give expected answer this 12% of the time. For this 12% of the time, it doesn't work not because of some wrong "if" condition or misplaced subroutine call. The debugging is almost always statistical debugging - there are no breakpoints to put or no watch to set or not even exceptions. So it takes pretty solid background in statistics and probability to effectively work in ML. And yes, most likely it would take much more than 2 years.
PS: For people who are saying you can "apply" DNNs in a day or learn it by a coursera course in 6 weeks - they are only very superficially right. Yeah, anyone can build ML model for a sample training data using tool in the same sense that anyone can compile sample code and have a working app. The problem is that most models don't work the first time as expected. The challenge lies in debugging the model and fix many of N possibilities to make it work. This is what working in ML is all about. It's like usual programming where it takes years of experience to debug the code and make it work for your purpose. The added twist in ML is that debugging is almost entirely statistical. When your model doesn't work, it doesn't work only in statistical sense. Your problem would be essentially that the model doesn't give expected answer this 12% of the time. For this 12% of the time, it doesn't work not because of some wrong "if" condition or misplaced subroutine call. The debugging is almost always statistical debugging - there are no breakpoints to put or no watch to set or not even exceptions. So it takes pretty solid background in statistics and probability to effectively work in ML. And yes, most likely it would take much more than 2 years.