diff --git a/docs/faq/finetune.md b/docs/faq/finetune.md
index 2c6c7e340279..533c3caf52a9 100644
--- a/docs/faq/finetune.md
+++ b/docs/faq/finetune.md
@@ -15,7 +15,7 @@ with these pretrained weights when training on our new task. This process is
 commonly called _fine-tuning_. There are a number of variations of fine-tuning.
 Sometimes, the initial neural network is used only as a _feature extractor_.
 That means that we freeze every layer prior to the output layer and simply learn
-a new output layer. In [another document](https://github.com/dmlc/mxnet-notebooks/blob/master/python/faq/predict.ipynb), we explained how to
+a new output layer. In [another document](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb), we explained how to
 do this kind of feature extraction. Another approach is to update all of
 the network's weights for the new task, and that's the approach we demonstrate in
 this document.
diff --git a/docs/faq/multi_devices.md b/docs/faq/multi_devices.md
index 5d538bca56af..b9cb3ea2916b 100644
--- a/docs/faq/multi_devices.md
+++ b/docs/faq/multi_devices.md
@@ -210,4 +210,4 @@ export PS_VERBOSE=1; python ../../tools/launch.py ...
 ### More
 
 - See more launch options by `python ../../tools/launch.py -h`
-- See more options of [ps-lite](http://ps-lite.readthedocs.org/en/latest/faq.html)
+- See more options of [ps-lite](https://ps-lite.readthedocs.io/en/latest)
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index aca091c41c3f..3eff299d7787 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -134,7 +134,7 @@ The Gluon and Module tutorials are in Python, but you can also find a variety of
 
 - [Imperative tensor operations on CPU/GPU](http://mxnet.incubator.apache.org/tutorials/basic/ndarray.html)
 
-- [NDArray Indexing](http://mxnet.incubator.apache.org/tutorials/basic/ndarray_indexing.html)
+- [NDArray Indexing](../tutorials/basic/ndarray_indexing.html)
 
 - [Symbol API](http://mxnet.incubator.apache.org/tutorials/basic/symbol.html)
 
@@ -174,7 +174,7 @@ The Gluon and Module tutorials are in Python, but you can also find a variety of
 
 <div class="applications">
 
-- [Connectionist Temporal Classification](http://mxnet.incubator.apache.org/tutorials/speech_recognition/ctc.html)
+- [Connectionist Temporal Classification](../tutorials/speech_recognition/ctc.html)
 
 - [Distributed key-value store](http://mxnet.incubator.apache.org/tutorials/python/kvstore.html)
 
diff --git a/python/mxnet/gluon/trainer.py b/python/mxnet/gluon/trainer.py
index 71c144f80cf2..c8822bb02c1d 100644
--- a/python/mxnet/gluon/trainer.py
+++ b/python/mxnet/gluon/trainer.py
@@ -16,7 +16,7 @@
 # under the License.
 
 # coding: utf-8
-# pylint: disable=
+# pylint: disable=line-too-long
 """Parameter optimizer."""
 __all__ = ['Trainer']
 
@@ -34,7 +34,7 @@ class Trainer(object):
         The set of parameters to optimize.
     optimizer : str or Optimizer
         The optimizer to use. See
-        `help <http://mxnet.io/api/python/optimization.html#the-mxnet-optimizer-package>`_
+        `help <http://mxnet.io/api/python/optimization/optimization.html#the-mxnet-optimizer-package>`_
         on Optimizer for a list of available optimizers.
     optimizer_params : dict
         Key-word arguments to be passed to optimizer constructor. For example,