Machine Learning Experiment Tracking

#artificialintelligence 

Once you have a number of models logged, you have way more dimensions to examine than can be looked at all at once. One powerful visualization tool we've discovered is the parallel coordinates chart. Here each line is an individual experiment and each column is an input hyperparameter or an output metric. I've highlighted the top accuracy runs and it shows quite clearly that across all of my experiments that I've selected, high accuracy comes from low dropout values. Aggregate metrics are good, but it is essential to look at specific examples.

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