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

Fine-tuning (deep learning)

Why can we make a big change with just a tiny tweak?

Neural network (machine learning)

Ever tried adjusting the learning rate like tuning a musical instrument?

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

How do we avoid overshooting in learning?

the compute-optimal training ratio is: roughly 20 tokens per parameter

How can we train AI efficiently without wasting resources?