I’m about to embark on a client facing Deep learning project where I’d need to get up to speed on some basic DL things and then start a research project within a couple of weeks or so. (they are aware of my current state of DL knowledge, but are hopeful that I can learn quickly an apply my subject matter expertise).
I’m looking at a software stack and the “safe” answer is python + pytorch, but I’d like to use Julia (for various reasons, some are indeed practical).
Is flux ready for a beginner to solve real client facing problems with? I do not want to jeopardize the project.
My plan is to work from fast.ai’s pytorch tutorials and then move onto the specific research problem, but I don’t want to invest time and then find out the fiddling with indices and model parameters in the translation or having to re-implement features will slow me down too much. I also don’t want to be in the situation of questioning whether some implementation issue or bug had derailed my experiment.
How likely are either of these scenarios? Could I ameliorate them easily?