diff --git a/earlystopping.md b/earlystopping.md
index 36969a469..8a92367e5 100644
--- a/earlystopping.md
+++ b/earlystopping.md
@@ -27,7 +27,7 @@ The best model is the one saved at the time of the vertical dotted line - i.e.,
 
 Using DL4J's early stopping functionality requires you to provide a number of configuration options:
 
-* A score calculator, such as the *DataSetLossCalculator*([JavaDoc](https://deeplearning4j.org/doc/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java)) for a Multi Layer Network, or *DataSetLossCalculatorCG* ([JavaDoc](https://deeplearning4j.org/doc/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java)) for a Computation Graph. Is used to calculate at every epoch (for example: the loss function value on a test set, or the accuracy on the test set)
+* A score calculator, such as the *DataSetLossCalculator*([JavaDoc](https://deeplearning4j.org/doc/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java)) for a Multi Layer Network, or *DataSetLossCalculatorCG* ([JavaDoc](https://deeplearning4j.org/doc/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java)) for a Computation Graph. These can be used to calculate for example the loss function value or the accuracy on a test set at every epoch. 
 * How frequently we want to calculate the score function (default: every epoch)
 * One or more termination conditions, which tell the training process when to stop. There are two classes of termination conditions:
   * Epoch termination conditions: evaluated every N epochs