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Jupyter notebooks are for data science, mostly because visualization is required. Something you do once, report it and it is done. Itsn't make sense to use Jupy

Jupyter notebooks are for data science, mostly because visualization is required. Something you do once, report it and it is done. Itsn't make sense to use Jupyter for other stuff. It doesn't make much sense to use them for training big models of deep learning because there are better tools for that.

rmetzler 8y ago
What tools can you recommend for machine learning?
- jorgemf 8y ago
  IT depends what you want to do
- cirgue 8y ago
  Scripts to train the model, Jupiter to do your analysis. This is, AFIK, what most ML shops do if they’re using python.
mertd 8y ago
> Something you do once, report it and it is done. I have been having a hard time getting comfortable with that idea. There is usually "a lot of" untested and unreviewed code in the notebook that produces the analysis.
- jorgemf 8y ago
  Well, there are many things to consider: - Code is small, most of it is calling well-know libraries with the algorithms. - Every 3-4 lines of code you usually show the results of what is happening (either how the data changed, a graph or whatever) - 95% of the errors in data science come from bad practices: you either didn't clean up the data correctly, you didn't split the train/test/validation sets correctly or at the right step in the process, you chosed the wrong algorithm, etc - most of the time you don't know whether you are doing something wrong or not because you don't have enough knowledge, but the code shows something all the time. No bugs in the code. Those are the reason I don't care very much about bugs in the code because usually there are bigger problems in the data science process rather than in the code.