Alle boeken

Professioneel

Apps Over Coach Inloggen Begin met lezen

Quantitative Finance · Begrippenlijst

Wat is Dropout, batch and layer normalisation?

Ook bekend als: dropout · batch normalisation · layer normalisation

Definition 7.4 Machine Learning for Markets · Hoofdstuk 7 — Neural Networks for Noisy Tabular Data

Dropout sets each hidden unit to zero with probability dd during training, scaling the others by 1/(1−d)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.

Validation R-squared of the small network after each epoch, three seeds; training stops five epochs after the best. The seeds peak at epochs 2, 2 and 4. Data: ml_nets.fitted.
Figure 7.2. Validation R-squared of the small network after each epoch, three seeds; training stops five epochs after the best. The seeds peak at epochs 2, 2 and 4. Data: ml_nets.fitted.
Lees in het hoofdstuk →

Verwante begrippen