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new 3b28e62 Change Straight Dope to Dive into Deep Learning (#14465)
3b28e62 is described below
commit 3b28e6236e75da404f88581bb8569f64b7fded74
Author: Aston Zhang <22279212+astonzh...@users.noreply.github.com>
AuthorDate: Thu Mar 21 00:22:27 2019 +0000
Change Straight Dope to Dive into Deep Learning (#14465)
---
README.md | 2 +-
docs/_static/mxnet-theme/navbar.html | 4 ++--
docs/api/perl/index.md | 2 +-
docs/community/ecosystem.md | 2 +-
docs/gluon/index.md | 14 +++++++-------
.../tutorials/gluon/gluon_from_experiment_to_deployment.md | 4 ++--
docs/tutorials/index.md | 2 +-
perl-package/AI-MXNet/lib/AI/MXNet.pm | 12 ++++++------
8 files changed, 21 insertions(+), 21 deletions(-)
diff --git a/README.md b/README.md
index dbc606d..0870f5c 100644
--- a/README.md
+++ b/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](./docs/tutorials/mkldnn/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)
diff --git a/docs/_static/mxnet-theme/navbar.html
b/docs/_static/mxnet-theme/navbar.html
index d39582e..d2449fa 100644
--- a/docs/_static/mxnet-theme/navbar.html
+++ b/docs/_static/mxnet-theme/navbar.html
@@ -11,7 +11,7 @@
<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 @@
</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>
diff --git a/docs/api/perl/index.md b/docs/api/perl/index.md
index 2d3d265..8815e33 100644
--- a/docs/api/perl/index.md
+++ b/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.
diff --git a/docs/community/ecosystem.md b/docs/community/ecosystem.md
index 101f76d..1e2bf07 100644
--- a/docs/community/ecosystem.md
+++ b/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
diff --git a/docs/gluon/index.md b/docs/gluon/index.md
index 469222e..ea3eedc 100644
--- a/docs/gluon/index.md
+++ b/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)
diff --git a/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
b/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
index 42a65b3..f84a72e 100644
--- a/docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
+++ b/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 -->
\ No newline at end of file
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index d429036..1fbfa92 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -52,7 +52,7 @@ 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.
diff --git a/perl-package/AI-MXNet/lib/AI/MXNet.pm
b/perl-package/AI-MXNet/lib/AI/MXNet.pm
index 80699b1..ffc72f9 100644
--- a/perl-package/AI-MXNet/lib/AI/MXNet.pm
+++ b/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
