[MXNET-1361] Change 'The Straight Dope' to 'Dive into Deep Learning' on the website

README.md

@@ -65,7 +65,7 @@ What's New
 * [Version 0.9.1 Release (NNVM refactor)](./docs/architecture/release_note_0_9.md) - NNVM branch is merged into master now. An official release will be made soon.
 * [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
 * [Updated Image Classification with new Pre-trained Models](./example/image-classification)
-* [Notebooks How to Use MXNet](https://github.com/zackchase/mxnet-the-straight-dope)
+* [Notebooks How to Use MXNet](https://github.com/d2l-ai/d2l-en)
 * [MKLDNN for Faster CPU Performance](./MKLDNN_README.md)
 * [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
 * [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)

docs/_static/mxnet-theme/navbar.html

@@ -11,7 +11,7 @@ <h1 id="logo-wrap">
           <a href="#" class="main-nav-link dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">Gluon <span class="caret"></span></a>
           <ul id="package-dropdown-menu" class="dropdown-menu navbar-menu">
             <li><a class="main-nav-link" href="{{url_root}}gluon/index.html">About</a></li>
-            <li><a class="main-nav-link" href="http://gluon.mxnet.io">The Straight Dope (Tutorials)</a></li>
+            <li><a class="main-nav-link" href="https://www.d2l.ai/">Dive into Deep Learning</a></li>
             <li><a class="main-nav-link" href="https://gluon-cv.mxnet.io">GluonCV Toolkit</a></li>
             <li><a class="main-nav-link" href="https://gluon-nlp.mxnet.io/">GluonNLP Toolkit</a></li>
           </ul>
@@ -108,7 +108,7 @@ <h1 id="logo-wrap">
               </li>
           </ul>
       </div>
-      
+
       <div class="plusIcon dropdown">
         <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button"><span class="glyphicon glyphicon-plus" aria-hidden="true"></span></a>
         <ul id="plusMenu" class="dropdown-menu dropdown-menu-right"></ul>

docs/api/perl/index.md

@@ -30,7 +30,7 @@ In addition please refer to [excellent metacpan doc interface](https://metacpan.
 [MXNet Python API Documentation](http://mxnet.io/api/python/index.html).
 
 AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. Please get acquainted with this new interface
-at [Deep Learning - The Straight Dope](http://gluon.mxnet.io/).
+at [Dive into Deep Learning](https://www.d2l.ai/).
 
 For specific Perl Gluon usage please refer to Perl examples and tests directories on github, but be assured that the Python and Perl usage
 are extremely close in order to make the use of the Python Gluon docs and examples as easy as possible.

docs/community/ecosystem.md

@@ -41,7 +41,7 @@ Community contributions to MXNet have added many new valuable features and funct
 
 * [Gluon 60 Minute Crash Course](https://gluon-crash-course.mxnet.io/) - deep learning practitioners can learn Gluon quickly with these six 10-minute tutorials.
     - [YouTube Series](https://www.youtube.com/playlist?list=PLkEvNnRk8uVmVKRDgznk3o3LxmjFRaW7s)
-* [The Straight Dope](https://gluon.mxnet.io/) - a series of notebooks designed to teach deep learning using the Gluon Python API for MXNet.
+* [Dive into Deep Learning](https://www.d2l.ai/) - a series of notebooks designed to teach deep learning using the Gluon Python API for MXNet.
 
 
 ## MXNet APIs

docs/gluon/index.md

@@ -25,7 +25,7 @@ To get started with Gluon, checkout the following resources and tutorials:
 * [60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/) - six 10-minute lessons on using Gluon
 * [GluonCV Toolkit](https://gluon-cv.mxnet.io/) - implementations of state of the art deep learning algorithms in **Computer Vision (CV)**
 * [GluonNLP Toolkit](https://gluon-nlp.mxnet.io/) - implementations of state of the art deep learning algorithms in **Natural Language Processing (NLP)**
-* [Gluon: The Straight Dope](https://gluon.mxnet.io/) - notebooks designed to teach deep learning from the ground up, all using the Gluon API
+* [Dive into Deep Learning](https://www.d2l.ai/) - notebooks designed to teach deep learning from the ground up, all using the Gluon API
 
 <br/>
 <div class="boxed">
@@ -42,14 +42,14 @@ To get started with Gluon, checkout the following resources and tutorials:
 
 <br/>
 <div class="boxed">
-    The Straight Dope
+    Dive into Deep Learning
 </div>
 
-The community is also working on parallel effort to create a foundational resource for learning about machine learning. The Straight Dope is a book composed of introductory as well as advanced tutorials – all based on the Gluon interface. For example,
+The community is also working on parallel effort to create a foundational resource for learning about machine learning. Dive into Deep Learning is a book composed of introductory as well as advanced tutorials – all based on the Gluon interface. For example,
 
-* [Learn about machine learning basics](http://gluon.mxnet.io/chapter01_crashcourse/introduction.html).
-* [Develop and train a simple neural network model](http://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html).
-* [Implement a Recurrent Neural Network (RNN) model for Language Modeling](http://gluon.mxnet.io/chapter05_recurrent-neural-networks/simple-rnn.html).
+* [Learn about machine learning basics](https://www.d2l.ai/chapter_introduction/intro.html).
+* [Develop and train a simple neural network model](https://www.d2l.ai/chapter_multilayer-perceptrons/mlp-scratch.html).
+* [Implement a Recurrent Neural Network (RNN) model for Language Modeling](https://www.d2l.ai/chapter_recurrent-neural-networks/rnn-scratch.html).
 
 <br/>
 <div class="boxed">
@@ -124,4 +124,4 @@ net.hybridize()
 * [60-minute Gluon Crash Course](https://gluon-crash-course.mxnet.io/)
 * [GluonCV Toolkit](https://gluon-cv.mxnet.io/)
 * [GluonNLP Toolkit](https://gluon-nlp.mxnet.io/)
-* [Gluon: The Straight Dope](https://gluon.mxnet.io/)
+* [Dive into Deep Learning](https://www.d2l.ai)

docs/tutorials/gluon/gluon_from_experiment_to_deployment.md

@@ -322,9 +322,9 @@ You can also find more ways to run inference and deploy your models here:
 ## References
 
 1. [Transfer Learning for Oxford102 Flower Dataset](https://github.com/Arsey/keras-transfer-learning-for-oxford102)
-2. [Gluon book on fine-tuning](https://gluon.mxnet.io/chapter08_computer-vision/fine-tuning.html)
+2. [Gluon book on fine-tuning](https://www.d2l.ai/chapter_computer-vision/fine-tuning.html)
 3. [Gluon CV transfer learning tutorial](https://gluon-cv.mxnet.io/build/examples_classification/transfer_learning_minc.html)
 4. [Gluon crash course](https://gluon-crash-course.mxnet.io/)
 5. [Gluon CPP inference example](https://github.com/apache/incubator-mxnet/blob/master/cpp-package/example/inference/)
 
-<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->

docs/tutorials/index.md

@@ -51,7 +51,7 @@ Another great resource for learning MXNet is our [examples section](https://gith
 
 We have two types of API available for Python: Gluon APIs and Module APIs. [See here](/api/python/gluon/gluon.html) for a comparison.
 
-A comprehensive introduction to Gluon can be found at [The Straight Dope](http://gluon.mxnet.io/). Structured like a book, it build up from first principles of deep learning and take a theoretical walkthrough of progressively more complex models using the Gluon API. Also check out the [60-Minute Gluon Crash Course](http://gluon-crash-course.mxnet.io/) if you're short on time or have used other deep learning frameworks before.
+A comprehensive introduction to Gluon can be found at [Dive into Deep Learning](http://www.d2l.ai/). Structured like a book, it build up from first principles of deep learning and take a theoretical walkthrough of progressively more complex models using the Gluon API. Also check out the [60-Minute Gluon Crash Course](http://gluon-crash-course.mxnet.io/) if you're short on time or have used other deep learning frameworks before.
 
 Use the tutorial selector below to filter to the relevant tutorials. You might see a download link in the top right corner of some tutorials. Use this to download a Jupyter Notebook version of the tutorial, and re-run and adjust the code as you wish.
 

perl-package/AI-MXNet/lib/AI/MXNet.pm

@@ -263,24 +263,24 @@ AI::MXNet - Perl interface to MXNet machine learning library
 =head1 DESCRIPTION
 
     Perl interface to MXNet machine learning library.
-    MXNet supports the Perl programming language. 
-    The MXNet Perl package brings flexible and efficient GPU computing and 
+    MXNet supports the Perl programming language.
+    The MXNet Perl package brings flexible and efficient GPU computing and
     state-of-art deep learning to Perl.
     It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
     It also lets you construct and customize the state-of-art deep learning models in Perl,
     and apply them to tasks, such as image classification and data science challenges.
 
     One important thing to internalize is that Perl interface is written to be as close as possible to the Python’s API,
-    so most, if not all of Python’s documentation and examples should just work in Perl after making few changes 
-    in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; 
+    so most, if not all of Python’s documentation and examples should just work in Perl after making few changes
+    in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ;
     and in 99% of cases that’s all that is needed there.
     In addition please refer to very detailed L<MXNet Python API Documentation|http://mxnet.io/api/python/index.html>.
 
     AI::MXNet supports new imperative PyTorch like Gluon MXNet interface.
-    Please get acquainted with this new interface at L<Deep Learning - The Straight Dope|https://gluon.mxnet.io/>.
+    Please get acquainted with this new interface at L<Dive into Deep Learning|https://www.d2l.ai/>.
 
     For specific Perl Gluon usage please refer to Perl examples and tests directories on github,
-    but be assured that the Python and Perl usage are extremely close in order to make the use 
+    but be assured that the Python and Perl usage are extremely close in order to make the use
     of the Python Gluon docs and examples as easy as possible.
 
     AI::MXNet is seamlessly glued with L<PDL|https://metacpan.org/pod/PDL>, the C++ level state can be easily initialized from PDL