I almost completed two big Data Science personal projects based on Deep Learning. They are the fanciest models I've implemented up to now, and I'm pushing all my code on GitHub.
Do you advice to upload trained models too? Or should I let other users run the code and get their own?
What do you do? What are pros and cons?
Topic rnn version-control deep-learning neural-network machine-learning
Category Data Science
From what I've seen on Github while looking for open source projects is that people usually do both.
You can have a section where one loads the models and runs the inference, and another section where you let the user train the models from scratch using your code.
I recommend doing this since some people do not want to retrain the model, especially if it's memory consuming (such as computer vision problems), where they only need to see the same results that you had. People who need to train models usually have a different need than what you had in your results, therefore you should let them tailor the results for their needs.
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