diff --git a/monai/handlers/mlflow_handler.py b/monai/handlers/mlflow_handler.py
index b3ee887983..b4ce4c384b 100644
--- a/monai/handlers/mlflow_handler.py
+++ b/monai/handlers/mlflow_handler.py
@@ -30,7 +30,7 @@
 class MLFlowHandler:
     """
     MLFlowHandler defines a set of Ignite Event-handlers for the MLFlow tracking logics.
-    It's can be used for any Ignite Engine(trainer, validator and evaluator).
+    It can be used for any Ignite Engine(trainer, validator and evaluator).
     And it can track both epoch level and iteration level logging, then MLFlow can store
     the data and visualize.
     The expected data source is Ignite ``engine.state.output`` and ``engine.state.metrics``.
@@ -41,6 +41,9 @@ class MLFlowHandler:
         - When ITERATION_COMPLETED, track expected item in
           ``self.output_transform(engine.state.output)`` in MLFlow, default to `Loss`.
 
+    Usage example is available in the tutorial:
+    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.
+
     Args:
         tracking_uri: connects to a tracking URI. can also set the `MLFLOW_TRACKING_URI` environment
             variable to have MLflow find a URI from there. in both cases, the URI can either be
diff --git a/monai/handlers/stats_handler.py b/monai/handlers/stats_handler.py
index f0fcf166e8..7d40f2a2f2 100644
--- a/monai/handlers/stats_handler.py
+++ b/monai/handlers/stats_handler.py
@@ -31,7 +31,7 @@
 class StatsHandler:
     """
     StatsHandler defines a set of Ignite Event-handlers for all the log printing logics.
-    It's can be used for any Ignite Engine(trainer, validator and evaluator).
+    It can be used for any Ignite Engine(trainer, validator and evaluator).
     And it can support logging for epoch level and iteration level with pre-defined loggers.
 
     Default behaviors:
@@ -39,6 +39,9 @@ class StatsHandler:
         - When ITERATION_COMPLETED, logs
           ``self.output_transform(engine.state.output)`` using ``self.logger``.
 
+    Usage example is available in the tutorial:
+    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.
+
     """
 
     def __init__(
diff --git a/monai/handlers/tensorboard_handlers.py b/monai/handlers/tensorboard_handlers.py
index 1dd89c7efb..411c197af6 100644
--- a/monai/handlers/tensorboard_handlers.py
+++ b/monai/handlers/tensorboard_handlers.py
@@ -64,7 +64,7 @@ def close(self):
 class TensorBoardStatsHandler(TensorBoardHandler):
     """
     TensorBoardStatsHandler defines a set of Ignite Event-handlers for all the TensorBoard logics.
-    It's can be used for any Ignite Engine(trainer, validator and evaluator).
+    It can be used for any Ignite Engine(trainer, validator and evaluator).
     And it can support both epoch level and iteration level with pre-defined TensorBoard event writer.
     The expected data source is Ignite ``engine.state.output`` and ``engine.state.metrics``.
 
@@ -73,6 +73,10 @@ class TensorBoardStatsHandler(TensorBoardHandler):
           ``engine.state.metrics`` to TensorBoard.
         - When ITERATION_COMPLETED, write each dictionary item in
           ``self.output_transform(engine.state.output)`` to TensorBoard.
+
+    Usage example is available in the tutorial:
+    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.
+
     """
 
     def __init__(
@@ -249,6 +253,9 @@ class TensorBoardImageHandler(TensorBoardHandler):
         - Expects ``output_transform(engine.state.output)`` to return a torch
           tensor in format (y_pred[N, channel, ...], loss).
 
+    Usage example is available in the tutorial:
+    https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/unet_segmentation_3d_ignite.ipynb.
+
     """
 
     def __init__(
diff --git a/monai/networks/nets/dynunet.py b/monai/networks/nets/dynunet.py
index 47644454ee..3e846b9b7b 100644
--- a/monai/networks/nets/dynunet.py
+++ b/monai/networks/nets/dynunet.py
@@ -78,6 +78,9 @@ class DynUNet(nn.Module):
     For example, if `strides=((1, 2, 4), 2, 1, 1)`, the minimal spatial size of the input is `(8, 16, 32)`, and
     the spatial size of the output is `(8, 8, 8)`.
 
+    Usage example with medical segmentation decathlon dataset is available at:
+    https://github.com/Project-MONAI/tutorials/tree/master/modules/dynunet_pipeline.
+
     Args:
         spatial_dims: number of spatial dimensions.
         in_channels: number of input channels.