Merge remote-tracking branch 'upstream/master'

doc/build.md

@@ -133,6 +133,7 @@ various distributed filesystem such as HDFS/Amazon S3/...
 #### Building with Intel MKL Support
 First, `source /path/to/intel/bin/compilervars.sh` to automatically set environment variables. Then, edit [make/config.mk](../make/config.mk), let `USE_BLAS = mkl`. `USE_INTEL_PATH = NONE` is usually not necessary to be modified.
 
+ - NOTICE: For `intel-mkl` version later than `parallel_studio_xe_2016.2.062`, you should use `source /path/to/intel/pkg_bin/compilervars.sh -arch intel64` instead where `-arch` must be specified according to your own architecture.
 
 ## Python Package Installation
 

example/README.md

@@ -48,7 +48,7 @@ If you want to contribute to this list and the examples, please open a new pull
 ###Web Predictive Services
 -----------------------
 * [MXNet Shinny](https://github.com/thirdwing/mxnet_shiny) - Source code for quickly creating a Shiny R app to host online image classification.
-
+* [Machine Eye] (http://rupeshs.github.io/machineye/)- Web service for local image file/image URL classification without uploading.
 ## List of tutorials
 
 ### Deep learning for hackers with MXnet
@@ -61,4 +61,7 @@ If you want to contribute to this list and the examples, please open a new pull
 
 ### Kaggle tutorials
 * [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (Python)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18079/end-to-end-deep-learning-tutorial-0-0392) - an end-to-end python tutorial for Kaggle heart disease diagnose competition (public leaderboard score 0.0392)
-* [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (R)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18122/deep-learning-model-in-r) - an end-to-end R tutorial for Kaggle heart disease diagnose competition
+* [Kaggle 2nd Annual Data Science Bowl End-to-End Deep Learning Tutorial (R)](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/18122/deep-learning-model-in-r) - an end-to-end R tutorial for Kaggle heart disease diagnose competition
+
+###Learning Note
+* [Learning Note in Chinese](https://github.com/zhubuntu/MXNet-Learning-Note) - Mxnet learning note in chinese.

example/kaggle-ndsb2/README.md

@@ -56,7 +56,7 @@ Prepare raw data in ```data``` folder. The tree of ```data``` folder is like
 4. We also provide the R code with the same network structure and parameters in ```Train.R```. Right now it used the pre-processed csv files by ```Preprocessing.py```. We will add the pre-processing R code later.
 
 Note:
-- To run with python2, you need to change ```Train.py, line #139``` to the python2 syntax.
+- To run with python2, you need to change ```Train.py, line #145 #199``` to the python2 syntax.
 - To modify network, change ```get_lenet``` function in ```Train.py``` or ```get.lenet``` function in ```Train.R```.
 - We also provide ```local_train```, ```local_test``` file for local parameter tuning.
 - To run on multiple GPU with huge network, or questions about saving network parameters etc, please refer [MXNet docs](https://mxnet.readthedocs.org/en/latest/)

example/kaggle-ndsb2/Train.py

@@ -196,7 +196,7 @@ def submission_helper(pred):
 fi = csv.reader(open("data/sample_submission_validate.csv"))
 f = open("submission.csv", "w")
 fo = csv.writer(f, lineterminator='\n')
-fo.writerow(fi.__next__())
+fo.writerow(fi.__next__()) # Python2: fo.writerow(fi.next())
 for line in fi:
     idx = line[0]
     key, target = idx.split('_')