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+Customized loss function
+======================================
+
+This tutorial provides guidelines for using customized loss function in network construction.
+
+
+Model Training Example
+----------
+
+Let's begin with a small regression example. We can build and train a regression model with the following code:
+
+
+ ```r
+    library(mxnet)
+    data(BostonHousing, package="mlbench")
+    train.ind = seq(1, 506, 3)
+    train.x = data.matrix(BostonHousing[train.ind, -14])
+    train.y = BostonHousing[train.ind, 14]
+    test.x = data.matrix(BostonHousing[-train.ind, -14])
+    test.y = BostonHousing[-train.ind, 14]
+    data <- mx.symbol.Variable("data")
+    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+    lro <- mx.symbol.LinearRegressionOutput(fc1)
+    mx.set.seed(0)
+    model <- mx.model.FeedForward.create(
+      lro, X=train.x, y=train.y,
+      eval.data=list(data=test.x, label=test.y),
+      ctx=mx.cpu(), num.round=10, array.batch.size=20,
+      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
+ ```
+
+Besides the `LinearRegressionOutput`, we also provide `LogisticRegressionOutput` and `MAERegressionOutput`.
+However, this might not be enough for real-world models. You can provide your own loss function
+by using `mx.symbol.MakeLoss` when constructing the network.
+
+
+How to Use Your Own Loss Function
+---------
+
+We still use our previous example.
+
+ ```r
+    library(mxnet)
+    data <- mx.symbol.Variable("data")
+    fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)
+    lro <- mx.symbol.MakeLoss(mx.symbol.square(mx.symbol.Reshape(fc1, shape = 0) - label))
+ ```
+
+In the last line of network definition, we do not use the predefined loss function. We define the loss
+by ourselves, which is `(pred-label)^2`.
+
+We have provided many operations on the symbols, so you can also define `|pred-label|` using the line below.
+
+ ```r
+    lro <- mx.symbol.MakeLoss(mx.symbol.abs(mx.symbol.Reshape(fc1, shape = 0) - label))
+ ```
+
+## Next Steps
+* [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
+* [Classify Real-World Images with a Pretrained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
+* [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html)
+* [Character Language Model Using RNN](http://mxnet.io/tutorials/r/charRnnModel.html)