A guide to Neptune.ai for tracking your experiments in Python
Every data scientist is familiar with experimentation.
You know the drill. You get a dataset, load it into a Jupyter notebook, explore it, preprocess the data, fit a baseline model or two, and then train an initial final model, such as XGBoost. The first time around, maybe you don’t tune the…
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