It's Not Insanity It's Stochastic Optimization

Einstein called it insanity. Machine learning engineers call it "Tuesday."

The beautiful irony here is that ML models literally work by doing the same thing over and over with slightly different random initializations, hoping for better results each time. Gradient descent? That's just fancy insanity with a learning rate. Training neural networks? Running the same forward and backward passes thousands of times while tweaking weights by microscopic amounts.

The difference between a broken algorithm and stochastic optimization is whether your loss function eventually goes down. If it does, you're a data scientist. If it doesn't, you're debugging at 3 AM questioning your life choices.

Fun fact: Stochastic optimization is just a sophisticated way of saying "let's add randomness and see what happens" – which is essentially controlled chaos with a PhD.