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Thanks! Will do. I work full time on data engineering/geospatial big data analytics, so I haven't had the energy to do this in the evenings or weekends yet. I do plenty of work with regression (but not in an MLOps sense) and dimensionality reduction (we do PCA). So in my mind my gap is (1) actual neural network work and (2) familiarity with workflows using e.g. pytorch or scikit-learn or something similar. Any pointers on where to get started resource-wise? Been thinking of starting with Ch.5 here and moving on from that: https://jakevdp.github.io/PythonDataScienceHandbook/. I have some projects in mind (including some predictive CFB model) so will start that up on the side while doing some of these tutorials.
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