ما معنى Dropout, batch and layer normalisation؟

Dropout sets each hidden unit to zero with probability $d$ during training, scaling the others by $1/(1 - d)$, and uses the full network at prediction. Batch normalisation standardises each hidden unit over the rows of the mini-batch (running averages at prediction); layer normalisation standardises the units of each row, independently of the batch.