Add usage note

python/basic/advanced_img_io.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Fast Image Processing with MXNet\n",
     "\n",
     "Previous tutorials have shown two ways of preprocessing images:\n",

python/basic/data.ipynb

@@ -4,6 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n",
+    "\n",
+    "Download the whole repository with command:\n",
+    "\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n",
+    "\n",
+    "Then run this notebook.\n",
+    "\n",
     "# Loading Data\n",
     "\n",
     "This tutorial we focus on how to feeding data into a training and inference program. We can manually copy data into a binded symbol as shown in the [mixed programming](./mixed.ipynb). Most training and inference modules in MXNet accepts data iterators, which simplifies this procedure, especially when reading large datasets from filesystems. Here we discuss the API conventions and several provided iterators. \n",
@@ -347,8 +355,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
@@ -362,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

python/basic/image_io.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Image Data IO\n",
     "This tutorial explains how to prepare, load and train with image data in MXNet. All IO in MXNet is handled via `mx.io.DataIter` and its subclasses, which is explained [here](./data.ipynb). In this tutorial we focus on how to use pre-built data iterators as while as custom iterators to process image data.\n",
     "\n",

python/basic/initializer.ipynb

@@ -6,6 +6,10 @@
     "collapsed": true
    },
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Parameter Initializer \n",
     "\n",
     "https://github.com/dmlc/mxnet/blob/master/python/mxnet/initializer.py\n"

python/basic/mixed.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Mixed Programing * \n",
     "\n",
     "In this tutorial we show how to combine NDArray and Symbol together to train a neural network from scratch. This mixed programming flavor is one of the unique feature that make MXNet different to other frameworks. The `MX` term in MXNet also often means \"mixed\". \n",

python/basic/module.ipynb

@@ -6,6 +6,10 @@
     "collapsed": true
    },
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Training and Inference Module\n",
     "\n",
     "We modularized commonly used codes for training and inference in the `module` (or `mod` for short) package. This package provides intermediate-level and high-level interface for executing predefined networks. \n",

python/basic/ndarray.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# NDArray Tutorial\n",
     "\n",
     "\n",

python/basic/optimizer.ipynb

@@ -6,6 +6,10 @@
     "collapsed": true
    },
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Optimizer\n",
     "\n",
     "In gradient-base optimization algorithms, we update the parameters (or weights) using the gradients in each iteration. We call this updating function as `Optimizer`. \n",

python/basic/record_io.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Python Record IO\n",
     "\n",
     "In [image_io](image_io.ipynb) we already learned how to pack image into standard recordio format and load it with ImageRecordIter. This tutorial will walk through the python interface for reading and writing record io files. It can be useful when you need more more control over the details of data pipeline. For example, when you need to augument image and label together for detection and segmentation, or when you need a custom data iterator for triplet sampling and negative sampling.\n",

python/basic/symbol.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Symbol Tutorial\n",
     "\n",
     "Besides the tensor computation interface [NDArray](./ndarray.ipynb), another main object in MXNet is the `Symbol` provided by `mxnet.symbol`, or `mxnet.sym` for short. A symbol represents a multi-output symbolic expression. They are composited by operators, such as simple matrix operations (e.g. “+”), or a neural network layer (e.g. convolution layer). An operator can take several input variables, produce more than one output variables, and have internal state variables. A variable can be either free, which we can bind with value later, or an output of another symbol. \n",

python/cnn/mnist.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Tutorial on Hand Written Digit Recognition\n",
     "\n",
     "In this tutorial we will go through the basic use case of MXNet and also touch on some advanced usages. This example is based on the MNIST dataset, which contains 70,000 images of hand written characters with 28-by-28 pixel size.\n",

python/how_to/finetune.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Fine-tune with Pre-trained Models\n",
     "\n",
     "In practice the dataset we use is relative small, so that we do not train an neural network from scratch, namely staring from random initialized parameters. Instead, it is common to train a neural network on a large-scale dataset and then use it either as an initialization or a fixed feature extractor. On [predict.ipynb](./predict.ipynb) we explained how to do the feature extraction, this tutorial will focus on how to use pre-trained model to fine tune a new network.\n",

python/how_to/predict.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Predict and Extract Features with Pre-trained Models\n",
     "\n",
     "This tutorial will work through how to use pre-trained models for predicting and feature extraction.\n",

python/recommendation_systems/cdl/collaborative-dl.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# MXNet for Collaborative Deep Learning in Recommender Systems\n",
     "In this tutorial, we build on MXNet to implement the Collaborative Deep Learning (CDL) [1] model for recommender systems.\n",
     "\n",

python/rnn/lstm.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# A Full Recipe for Recurrent LSTM Model\n",
     "\n",
     "## Introduction\n",

python/tutorials/char_lstm.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Character-level language models\n",
     "\n",
     "This tutorial shows how to train a character-level language model with a multilayer recurrent neural network. In particular, we will train a multilayer LSTM network that is able to generate President Obama's speeches.\n",
@@ -410,8 +414,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python2"
   },
@@ -425,7 +430,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

python/tutorials/linear-regression.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# MXNet Basics - Linear Regression using MXNet"
    ]
   },

python/tutorials/matrix_factorization.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Matrix Factorization\n",
     "\n",
     "In a recommendation system, there is a group of users and a set of items. Given that each users have rated some items in the system, we would like to predict how the users would rate the items that they have not yet rated, such that we can make recommendations to the users.\n",

python/tutorials/mnist.ipynb

@@ -6,6 +6,10 @@
     "collapsed": true
    },
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Handwritten Digit Recognition\n",
     "\n",
     "This tutorial guides you through a classic computer vision application: identify hand written digits with neural networks. \n",

python/tutorials/predict_imagenet.ipynb

@@ -4,6 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Predict with pre-trained models\n",
     "\n",
     "This is a demo for predicting with a pre-trained model on the full imagenet dataset, which contains over 10 million images and 10 thousands classes. For a more detailed explanation, please refer to [predict.ipynb](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb).\n",

python/tutorials/understanding_vanish_gradient.ipynb

@@ -6,6 +6,10 @@
     "collapsed": true
    },
    "source": [
+    "# Pre-requisites to run this notebook\n\n",
+    "Download the whole repository with command:\n\n",
+    "git clone https://github.com/dmlc/mxnet-notebooks.git\n\n",
+    "Then run this notebook.\n\n",
     "# Understanding the vanishing gradient problem through visualization"
    ]
   },