diff --git a/docs/tutorials/r/symbol.md b/docs/tutorials/r/symbol.md
index 63f3a53bcaaa..6ab4dc2d3d31 100644
--- a/docs/tutorials/r/symbol.md
+++ b/docs/tutorials/r/symbol.md
@@ -104,7 +104,7 @@ In the example, *net* is used as a function to apply to an existing symbol
 
 ## Training a Neural Net
 
-The [model API](../../../R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
+The [model API](https://github.com/apache/incubator-mxnet/blob/master/R-package/R/model.R) is a thin wrapper around the symbolic executors to support neural net training.
 
 We encourage you to read [Symbolic Configuration and Execution in Pictures for python package](../../api/python/symbol_in_pictures/symbol_in_pictures.md)for a detailed explanation of concepts in pictures.
 
diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md
index e2f0a12c0fda..6a69341da985 100644
--- a/docs/tutorials/sparse/row_sparse.md
+++ b/docs/tutorials/sparse/row_sparse.md
@@ -271,7 +271,7 @@ rsp_retained = mx.nd.sparse.retain(rsp, mx.nd.array([0, 1]))
 
 ## Sparse Operators and Storage Type Inference
 
-Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](https://mxnet.io/versions/master/api/python/ndarray/sparse.html) to find what sparse operators are available.
+Operators that have specialized implementation for sparse arrays can be accessed in ``mx.nd.sparse``. You can read the [mxnet.ndarray.sparse API documentation](http://mxnet.io/versions/master/api/python/ndarray/sparse.html) to find what sparse operators are available.
 
 
 ```python
diff --git a/docs/tutorials/sparse/train.md b/docs/tutorials/sparse/train.md
index d6e3f4e82af2..22ce039ee7f5 100644
--- a/docs/tutorials/sparse/train.md
+++ b/docs/tutorials/sparse/train.md
@@ -99,7 +99,7 @@ f = mx.sym.sparse.elemwise_add(c, c)
 ### Storage Type Inference
 
 What will be the output storage types of sparse symbols? In MXNet, for any sparse symbol, the result storage types are inferred based on storage types of inputs.
-You can read the [Sparse Symbol API](https://mxnet.io/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
+You can read the [Sparse Symbol API](http://mxnet.io/versions/master/api/python/symbol/sparse.html) documentation to find what output storage types are. In the example below we will try out the storage types introduced in the Row Sparse and Compressed Sparse Row tutorials: `default` (dense), `csr`, and `row_sparse`.
 
 
 ```python