LAMB optimizer does: layer-wise adaptive learning rates for large batch training

gradient accumulation simulates larger batch sizes without more memory

Can you train a machine like you do with a computer?

batch size affects generalization: larger batches find sharper minima

Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates

instruction tuning does: fine-tunes on (instruction, response) pairs

Fine-tuning adapts pre-trained models to new tasks

AdaGrad does: divides learning rate by sqrt of sum of squared gradients

How do we avoid overshooting in learning?

Proximal gradient methods for learning

Why can't we always find the best path in a maze?

Adam combines momentum and RMSprop: adapts per-parameter learning rates

Ever wondered how a car adjusts its speed for smooth turns?