If you want to raise your salary from $10 to $20 per hour, playing with existing models is the way to go.
If you want to make serious money solving real problems, take the time to learn about automated differentiation and all the related mathematics about how gradients flow backwards through the network.
But like the coding slave (great nick BTW) said, first play a bit, then learn how it works. Image transformation GANs are a lot of fun.
Here's why the learning part will be crucial to differentiate you from all the clueless outsourced cheap labor:
Recently, there has been a load of new AI papers by so-called scientists on optical flow, and even the greatest new approaches using millions of parameters and costing hundreds of thousands of dollars to train still DO NOT reach the general level of quality that the 2004 census transform approach had.
Similarly, there have been high-profile papers where people randomly chained together TensorFlow operations to build their loss function, oblivious to the fact that some intermediate operations were not differentiable and, hence, their loss would never back-propagate. As a result, all of their claims had to be fraudulent because one could mathematically prove that their network was incapable of learning.
The larger AI competitions have by now limited the number of submissions that teams are allowed to make per week, simply to discourage people from trying to guess the test results when their AI doesn't work as it should.
Or consider the Uber pedestrian fatality where their neural network was overtrained ( = bad loss function ) to the point where it was unwilling to recognize bicycles at night.
And lastly, not knowing about gradient descent will just waste boatloads of money by 100x-ing your training time. Most stereo disparity and depth estimation AI papers use loss functions that only work on adjacent pixels. That means for a single correction to propagate to all pixels in a HD frame, you'll need 1920 iterations when only 1 could be sufficient.
You will find that my examples are all from autonomous driving. That's because here the discrepancy between GPU-powered brute force amateurs and skilled professionals is the most striking. German luxury cars have integrated lane-keeping, street sign recognition, and safety distance keeping for 10+ years, so for those tasks there are proven algorithms that work on a Pentium III in real time. And now there's lots of NVIDIA GPU kiddies trying to reinvent the wheel with limited success.
For your future employer, you having a firm grasp of how gradients work is the difference between mediocre and state of the art results, and between affordable and too expensive. So if there is one single AI skill that is both exhausting to learn and crucially important, it is differentiation and gradient flow.