tl;dr: Agreed, but the problems and models ML folks play with are more complex than most physics-like systems. Having spent a lot of time doing Monte Carlo ren

tl;dr: Agreed, but the problems and models ML folks play with are more complex than most physics-like systems. Having spent a lot of time doing Monte Carlo rendering, I can say that "why is this pixel messed up" can be a painful affair (and that domain is at least visual!). Bad input geometry, NaNs or Infs because you forgot to handle some 1/r^2 term, etc. occur in all numerical computing. Some of this is actually obviated in TensorFlow the same way it is in much of numerical computing: frameworks that have been battle hardened through years of bug fixes. This lets you at least focus on "Did I describe my model correctly?" rather than "Did I implement gradient descent correctly too?". Again, rendering (and all physical simulation) has the benefit of producing a picture and the inputs are effectively "physical". The challenge for machine learning is that you're trying to optimize for figuring out the underlying model rather than relying on the laws of physics for your problem. While this is just as true in any numerical modeling problem ("Should this be a 4th degree polynomial or just cubic?"), the models that ML folks put together are effectively quite complex and the input datasets are both very large and usually unstructured. The visualization of the network being trained in the TF Playground (http://playground.tensorflow.org http://playground.tensorflow.org) helps with the model but not the original space.