Edit ‘the_seventy_maxims_of_maximally_effective_machine_learning_engineers’

the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco

@@ -13,7 +13,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. Every dataset is trainable—at least once.
 *. A gentle learning rate turneth away divergence. Once the loss stabilizes, crank it up.
 *. Do unto others’ hyperparameters as you would have them do unto yours.
-*. “Innovative architecture” means never asking, “What’s the worst thing this could hallucinate?”
+*. “Innovative architecture” means never asking “did we implement the baseline correctly?”
 *. Only you can prevent vanishing gradients.
 *. Your model is in the leaderboards: be sure it has dropout.
 *. The longer training goes without overfitting, the bigger the validation-set disaster.