How to Use DVC for Tuning Hyperparameters in Machine Learning
Hyperparameters are the values that define your machine learning model. They are different from model parameters because we can't get them from training our model. Optimizing these values means running training steps for different kinds of models. We can get the best model by iterating through different hyperparameter values and seeing how they affect our accuracy. With DVC, we can add some automation to the tuning process and be able to find and restore any really good models that emerge. We'll do an example of this with grid search in DVC first.
Software/Hardware Engineer | International tech speaker | Random inventor and slightly mad scientist
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