[GitHub] aaronmarkham commented on a change in pull request #13411: [WIP] Gluon end to end tutorial

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aaronmarkham commented on a change in pull request #13411: [WIP] Gluon end to end tutorial
URL: https://github.com/apache/incubator-mxnet/pull/13411#discussion_r245325207
 
 

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 File path: docs/tutorials/gluon/gluon_from_experiment_to_deployment.md
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+# Gluon: from experiment to deployment, an end to end example
+
+## Overview
+MXNet Gluon API comes with a lot of great features and it can provide you everything you need from experiment to deploy the model. In this tutorial, we will walk you through a common use case on how to build a model using gluon, train it on your data, and deploy it for inference.
+
+Let's say you need to build a service that provides flower species recognition. A common use case is, you don't have enough data to train a good model. In such cases we use a technique called Transfer Learning.
+In Transfer Learning we make use of a pre-trained model that solves a related task but is trained on a very large standard dataset such as ImageNet from a different domain, we utilize the knowledge in this pre-trained model to perform a new task at hand.
+
+Gluon provides State of the Art models for many of the standard tasks such as Classification, Object Detection, Segmentation, etc. In this tutorial we will use the pre-trained model [ResNet50 V2](https://arxiv.org/abs/1603.05027) trained on ImageNet dataset, this model achieves 77.11% top-1 accuracy on ImageNet, we seek to transfer as much knowledge as possible for our task of recognizing different species of Flowers.
+
+In this tutorial we will show you the steps to load pre-trained model from Gluon, tweak the model according to your need, fine-tune the model on your small dataset, and finally deploy the trained model to integrate with your service.
 
 Review comment:
   You already said this in line 5.
   ```suggestion
   
   ```

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