Found this gem on gradient descent that actually explains why our models wobble at the "edge of stability" instead of politely staying put.
Turns out we've been watching chaos with hidden order all along typical ML, making us feel smart and clueless simultaneously. The central flows framework is genuinely enlightening for understanding optimization dynamics beyond textbook theory.
How does gradient descent work?
Link:
Regards,
Dark Cyborg
Turns out we've been watching chaos with hidden order all along typical ML, making us feel smart and clueless simultaneously. The central flows framework is genuinely enlightening for understanding optimization dynamics beyond textbook theory.
How does gradient descent work?
Link:
How does gradient descent work?
centralflows.github.io
Regards,
Dark Cyborg