This is an automated email from the ASF dual-hosted git repository.
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The following commit(s) were added to refs/heads/master by this push:
new a21d3e0 Fix more broken links (#7480)
a21d3e0 is described below
commit a21d3e0526588c1bbe7efcf8a93e9108dfb207b5
Author: Sandeep Krishnamurthy <sandeep.krishn...@gmail.com>
AuthorDate: Tue Aug 15 11:01:22 2017 -0700
Fix more broken links (#7480)
---
docs/get_started/windows_setup.md | 2 +-
docs/model_zoo/index.md | 10 +++++-----
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/docs/get_started/windows_setup.md
b/docs/get_started/windows_setup.md
index 86104c6..d695c59 100755
--- a/docs/get_started/windows_setup.md
+++ b/docs/get_started/windows_setup.md
@@ -23,7 +23,7 @@ This produces a library called ```libmxnet.dll```.
To build and install MXNet yourself, you need the following dependencies.
Install the required dependencies:
1. If [Microsoft Visual Studio 2013](https://www.visualstudio.com/downloads/)
is not already installed, download and install it. You can download and install
the free community edition.
-2. Install [Visual C++ Compiler Nov 2013
CTP](https://www.microsoft.com/en-us/download/details.aspx?id=41151).
+2. Install [Visual C++
Compiler](http://landinghub.visualstudio.com/visual-cpp-build-tools).
3. Back up all of the files in the ```C:\Program Files (x86)\Microsoft Visual
Studio 12.0\VC``` folder to a different location.
4. Copy all of the files in the ```C:\Program Files (x86)\Microsoft Visual C++
Compiler Nov 2013 CTP``` folder (or the folder where you extracted the zip
archive) to the ```C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC```
folder, and overwrite all existing files.
5. Download and install
[OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
diff --git a/docs/model_zoo/index.md b/docs/model_zoo/index.md
index a5a2b32..19811f2 100644
--- a/docs/model_zoo/index.md
+++ b/docs/model_zoo/index.md
@@ -32,7 +32,7 @@ Convolutional neural networks are the state-of-art
architecture for many image a
* [Places2](http://places2.csail.mit.edu/download.html): There are 1.6 million
train images from 365 scene categories in the Places365-Standard, which are
used to train the Places365 CNNs. There are 50 images per category in the
validation set and 900 images per category in the testing set. Compared to the
train set of Places365-Standard, the train set of Places365-Challenge has 6.2
million extra images, leading to totally 8 million train images for the
Places365 challenge 2016. The vali [...]
* [Multimedia
Commons](https://aws.amazon.com/public-datasets/multimedia-commons/): YFCC100M
(99.2 million images and 0.8 million videos from Flickr) and supplemental
material (pre-extracted features, additional annotations).
-For instructions on using these models, see [the python tutorial on using
pre-trained ImageNet
models](http://mxnet.io/tutorials/python/predict_imagenet.html).
+For instructions on using these models, see [the python tutorial on using
pre-trained ImageNet
models](https://mxnet.incubator.apache.org/tutorials/python/predict_image.html).
| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
| --- | --- | --- | --- | --- |
@@ -53,19 +53,19 @@ For instructions on using these models, see [the python
tutorial on using pre-tr
## Recurrent Neural Networks (RNNs) including LSTMs
-MXNet supports many types of recurrent neural networks (RNNs), including Long
Short-Term Memory
([LSTM](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf))
+MXNet supports many types of recurrent neural networks (RNNs), including Long
Short-Term Memory ([LSTM](http://www.bioinf.jku.at/publications/older/2604.pdf))
and Gated Recurrent Units (GRU) networks. Some available datasets include:
-* [Penn Treebank (PTB)](https://www.cis.upenn.edu/~treebank/): Text corpus
with ~1 million words. Vocabulary is limited to 10,000 words. The task is
predicting downstream words/characters.
+* [Penn Treebank (PTB)](https://catalog.ldc.upenn.edu/LDC95T7): Text corpus
with ~1 million words. Vocabulary is limited to 10,000 words. The task is
predicting downstream words/characters.
* [Shakespeare](http://cs.stanford.edu/people/karpathy/char-rnn/): Complete
text from Shakespeare's works.
-* [IMDB reviews](https://s3.amazonaws.com/text-datasets): 25,000 movie
reviews, labeled as positive or negative
+* [IMDB
reviews](https://getsatisfaction.com/imdb/topics/imdb-data-now-available-in-amazon-s3):
25,000 movie reviews, labeled as positive or negative
* [Facebook bAbI](https://research.facebook.com/researchers/1543934539189348):
As a set of 20 question & answer tasks, each with 1,000 training examples.
* [Flickr8k, COCO](http://mscoco.org/): Images with associated caption
(sentences). Flickr8k consists of 8,092 images captioned by AmazonTurkers with
~40,000 captions. COCO has 328,000 images, each with 5 captions. The COCO
images also come with labeled objects using segmentation algorithms.
| Model Definition | Dataset | Model Weights | Research Basis | Contributors |
| --- | --- | --- | --- | --- |
-| LSTM - Image Captioning | Flickr8k, MS COCO | | [Vinyals et al..,
2015](https://arxiv.org/pdf/ 1411.4555v2.pdf) | @... |
+| LSTM - Image Captioning | Flickr8k, MS COCO | | [Vinyals et al..,
2015](https://arxiv.org/pdf/1411.4555.pdf) | @... |
| LSTM - Q&A System| bAbl | | [Weston et al..,
2015](https://arxiv.org/pdf/1502.05698v10.pdf) | |
| LSTM - Sentiment Analysis| IMDB | | [Li et al..,
2015](http://arxiv.org/pdf/1503.00185v5.pdf) | |
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