From 2a68371a4aedf937889c378c75423eebacf15acb Mon Sep 17 00:00:00 2001 From: Aston Zhang Date: Mon, 18 Mar 2019 17:11:58 -0700 Subject: [PATCH] Change Straight Dope to Dive into Deep Learning --- 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 +++++++------- .../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 307bbd432c27..86b0dc637cbc 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](./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 d39582ecccae..d2449fa76e1b 100644 --- a/docs/_static/mxnet-theme/navbar.html +++ b/docs/_static/mxnet-theme/navbar.html @@ -11,7 +11,7 @@

Gluon @@ -108,7 +108,7 @@

- +
diff --git a/docs/api/perl/index.md b/docs/api/perl/index.md index 2d3d2656d7cd..8815e33a3e39 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 101f76d4c380..1e2bf07335d3 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 469222e46293..ea3eedce065e 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
@@ -42,14 +42,14 @@ To get started with Gluon, checkout the following resources and tutorials:
- The Straight Dope + Dive into Deep Learning
-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).
@@ -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 42a65b352199..f84a72ef1063 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/) - \ No newline at end of file + diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 7e0ffaa3f72a..d08bebd578ba 100644 --- a/docs/tutorials/index.md +++ b/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. diff --git a/perl-package/AI-MXNet/lib/AI/MXNet.pm b/perl-package/AI-MXNet/lib/AI/MXNet.pm index 80699b14311c..ffc72f9513d3 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. AI::MXNet supports new imperative PyTorch like Gluon MXNet interface. - Please get acquainted with this new interface at L. + Please get acquainted with this new interface at L. 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, the C++ level state can be easily initialized from PDL