From 2b67436802b750e15b9fbfdf275958c1000be6a8 Mon Sep 17 00:00:00 2001 From: Junyuan Xie Date: Sun, 3 Dec 2017 17:09:27 -0800 Subject: [PATCH] Update index.md --- docs/tutorials/index.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md index 325f385c..d20a8211 100644 --- a/docs/tutorials/index.md +++ b/docs/tutorials/index.md @@ -1,14 +1,11 @@ # Tutorials -These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier. - - ## Gluon Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency. -This is a selected subset of Gluon tutorials. For the comprehensive tutorial on Gluon, -please see [gluon.mxnet.io](http://gluon.mxnet.io). + +This is a selected subset of Gluon tutorials that explains basic usage of Gluon and fundamental concepts in deep learning. For the comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see [gluon.mxnet.io](http://gluon.mxnet.io). ### Basics @@ -32,6 +29,8 @@ please see [gluon.mxnet.io](http://gluon.mxnet.io). ## MXNet +These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier. + ### Basics ```eval_rst