The learning rate sets how far each weight moves along its gradient on every update. The gradient gives the direction. The learning rate gives the step size. It is usually the first thing to tune, because a wrong value ruins a run no matter how good the architecture is.
Set it too high and the steps overshoot the valley. Loss jumps around, or shoots to not-a-number as weights blow up. Set it too low and training crawls. You burn hours of compute and may stall on a flat stretch before reaching anything useful.
Most teams start around 0.001 with Adam, watch the first few hundred steps, then adjust by factors of ten. Decaying the rate later in training helps the weights settle instead of bouncing near the bottom.
Rewriting in plainer words…
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