From 5a77d8f2d2414abeaf430ebd0e276f757d10dc76 Mon Sep 17 00:00:00 2001 From: Wang Date: Wed, 15 Mar 2017 17:18:59 -0700 Subject: [PATCH 1/4] Add usgae note for notebooks --- python/basic/advanced_img_io.ipynb | 1 + python/basic/data.ipynb | 1 + python/basic/image_io.ipynb | 1 + python/basic/initializer.ipynb | 1 + python/basic/mixed.ipynb | 1 + python/basic/module.ipynb | 1 + python/basic/ndarray.ipynb | 1 + python/basic/optimizer.ipynb | 1 + python/basic/record_io.ipynb | 1 + python/basic/symbol.ipynb | 1 + python/cnn/mnist.ipynb | 1 + python/how_to/finetune.ipynb | 1 + python/how_to/predict.ipynb | 1 + python/recommendation_systems/cdl/collaborative-dl.ipynb | 1 + python/rnn/lstm.ipynb | 1 + python/tutorials/char_lstm.ipynb | 6 ++++-- python/tutorials/linear-regression.ipynb | 1 + python/tutorials/matrix_factorization.ipynb | 1 + python/tutorials/mnist.ipynb | 1 + python/tutorials/predict_imagenet.ipynb | 1 + python/tutorials/understanding_vanish_gradient.ipynb | 1 + 21 files changed, 24 insertions(+), 2 deletions(-) diff --git a/python/basic/advanced_img_io.ipynb b/python/basic/advanced_img_io.ipynb index ac9405a4c..19fff68bc 100644 --- a/python/basic/advanced_img_io.ipynb +++ b/python/basic/advanced_img_io.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# Fast Image Processing with MXNet\n", "\n", "Previous tutorials have shown two ways of preprocessing images:\n", diff --git a/python/basic/data.ipynb b/python/basic/data.ipynb index ea7f13d07..49057b812 100644 --- a/python/basic/data.ipynb +++ b/python/basic/data.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/image_io.ipynb b/python/basic/image_io.ipynb index ffdb867a5..ec0ea8f4d 100644 --- a/python/basic/image_io.ipynb +++ b/python/basic/image_io.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/initializer.ipynb b/python/basic/initializer.ipynb index c42f8cdec..63ea8c5af 100644 --- a/python/basic/initializer.ipynb +++ b/python/basic/initializer.ipynb @@ -6,6 +6,7 @@ "collapsed": true }, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# Parameter Initializer \n", "\n", "https://github.com/dmlc/mxnet/blob/master/python/mxnet/initializer.py\n" diff --git a/python/basic/mixed.ipynb b/python/basic/mixed.ipynb index cd6f35c7c..166f0ffb3 100644 --- a/python/basic/mixed.ipynb +++ b/python/basic/mixed.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/module.ipynb b/python/basic/module.ipynb index d1ca1ea37..898c83944 100644 --- a/python/basic/module.ipynb +++ b/python/basic/module.ipynb @@ -6,6 +6,7 @@ "collapsed": true }, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/ndarray.ipynb b/python/basic/ndarray.ipynb index 50cd76a81..25c79fa8a 100644 --- a/python/basic/ndarray.ipynb +++ b/python/basic/ndarray.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# NDArray Tutorial\n", "\n", "\n", diff --git a/python/basic/optimizer.ipynb b/python/basic/optimizer.ipynb index 2fd40d118..faf9543a5 100644 --- a/python/basic/optimizer.ipynb +++ b/python/basic/optimizer.ipynb @@ -6,6 +6,7 @@ "collapsed": true }, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/record_io.ipynb b/python/basic/record_io.ipynb index 957f58a4e..16a4ce9f9 100644 --- a/python/basic/record_io.ipynb +++ b/python/basic/record_io.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/basic/symbol.ipynb b/python/basic/symbol.ipynb index bf2396a85..9ce21cc5b 100644 --- a/python/basic/symbol.ipynb +++ b/python/basic/symbol.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/cnn/mnist.ipynb b/python/cnn/mnist.ipynb index f1d4cca9d..050cdad78 100644 --- a/python/cnn/mnist.ipynb +++ b/python/cnn/mnist.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/how_to/finetune.ipynb b/python/how_to/finetune.ipynb index 8eb5e529b..5aea75a2e 100644 --- a/python/how_to/finetune.ipynb +++ b/python/how_to/finetune.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/how_to/predict.ipynb b/python/how_to/predict.ipynb index 9d0e01d9d..fd2c28a47 100644 --- a/python/how_to/predict.ipynb +++ b/python/how_to/predict.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/recommendation_systems/cdl/collaborative-dl.ipynb b/python/recommendation_systems/cdl/collaborative-dl.ipynb index dbca98050..ae8aa4851 100644 --- a/python/recommendation_systems/cdl/collaborative-dl.ipynb +++ b/python/recommendation_systems/cdl/collaborative-dl.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/rnn/lstm.ipynb b/python/rnn/lstm.ipynb index 70845898d..6e2440fc0 100644 --- a/python/rnn/lstm.ipynb +++ b/python/rnn/lstm.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# A Full Recipe for Recurrent LSTM Model\n", "\n", "## Introduction\n", diff --git a/python/tutorials/char_lstm.ipynb b/python/tutorials/char_lstm.ipynb index 3f950403c..58171c964 100644 --- a/python/tutorials/char_lstm.ipynb +++ b/python/tutorials/char_lstm.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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 +411,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -425,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/python/tutorials/linear-regression.ipynb b/python/tutorials/linear-regression.ipynb index 343964dcb..7ec180ee5 100644 --- a/python/tutorials/linear-regression.ipynb +++ b/python/tutorials/linear-regression.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# MXNet Basics - Linear Regression using MXNet" ] }, diff --git a/python/tutorials/matrix_factorization.ipynb b/python/tutorials/matrix_factorization.ipynb index 7df6cee49..b998aa39e 100644 --- a/python/tutorials/matrix_factorization.ipynb +++ b/python/tutorials/matrix_factorization.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/tutorials/mnist.ipynb b/python/tutorials/mnist.ipynb index 79cf7c796..39d30696c 100644 --- a/python/tutorials/mnist.ipynb +++ b/python/tutorials/mnist.ipynb @@ -6,6 +6,7 @@ "collapsed": true }, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# Handwritten Digit Recognition\n", "\n", "This tutorial guides you through a classic computer vision application: identify hand written digits with neural networks. \n", diff --git a/python/tutorials/predict_imagenet.ipynb b/python/tutorials/predict_imagenet.ipynb index 68baafe5d..fe44fb9c0 100644 --- a/python/tutorials/predict_imagenet.ipynb +++ b/python/tutorials/predict_imagenet.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\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", diff --git a/python/tutorials/understanding_vanish_gradient.ipynb b/python/tutorials/understanding_vanish_gradient.ipynb index da4654d3c..f1612e88c 100644 --- a/python/tutorials/understanding_vanish_gradient.ipynb +++ b/python/tutorials/understanding_vanish_gradient.ipynb @@ -6,6 +6,7 @@ "collapsed": true }, "source": [ + "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", "# Understanding the vanishing gradient problem through visualization" ] }, From b20ee51453818bd8415a0a364fba369bff792217 Mon Sep 17 00:00:00 2001 From: Wang Date: Wed, 15 Mar 2017 17:43:13 -0700 Subject: [PATCH 2/4] Reformat --- python/basic/advanced_img_io.ipynb | 4 +++- python/basic/data.ipynb | 4 +++- python/basic/image_io.ipynb | 4 +++- python/basic/initializer.ipynb | 4 +++- python/basic/mixed.ipynb | 4 +++- python/basic/module.ipynb | 5 +++-- python/basic/ndarray.ipynb | 4 +++- python/basic/optimizer.ipynb | 4 +++- python/basic/record_io.ipynb | 4 +++- python/basic/symbol.ipynb | 4 +++- python/cnn/mnist.ipynb | 4 +++- python/how_to/finetune.ipynb | 4 +++- python/how_to/predict.ipynb | 4 +++- python/recommendation_systems/cdl/collaborative-dl.ipynb | 4 +++- python/rnn/lstm.ipynb | 4 +++- python/tutorials/char_lstm.ipynb | 4 +++- python/tutorials/linear-regression.ipynb | 4 +++- python/tutorials/matrix_factorization.ipynb | 4 +++- python/tutorials/mnist.ipynb | 4 +++- python/tutorials/predict_imagenet.ipynb | 4 +++- python/tutorials/understanding_vanish_gradient.ipynb | 4 +++- 21 files changed, 63 insertions(+), 22 deletions(-) diff --git a/python/basic/advanced_img_io.ipynb b/python/basic/advanced_img_io.ipynb index 19fff68bc..0dd083a16 100644 --- a/python/basic/advanced_img_io.ipynb +++ b/python/basic/advanced_img_io.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# Fast Image Processing with MXNet\n", "\n", "Previous tutorials have shown two ways of preprocessing images:\n", diff --git a/python/basic/data.ipynb b/python/basic/data.ipynb index 49057b812..d31f42b10 100644 --- a/python/basic/data.ipynb +++ b/python/basic/data.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/basic/image_io.ipynb b/python/basic/image_io.ipynb index ec0ea8f4d..eaa5f1cc1 100644 --- a/python/basic/image_io.ipynb +++ b/python/basic/image_io.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/basic/initializer.ipynb b/python/basic/initializer.ipynb index 63ea8c5af..15f974af0 100644 --- a/python/basic/initializer.ipynb +++ b/python/basic/initializer.ipynb @@ -6,7 +6,9 @@ "collapsed": true }, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# Parameter Initializer \n", "\n", "https://github.com/dmlc/mxnet/blob/master/python/mxnet/initializer.py\n" diff --git a/python/basic/mixed.ipynb b/python/basic/mixed.ipynb index 166f0ffb3..cef956e47 100644 --- a/python/basic/mixed.ipynb +++ b/python/basic/mixed.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/basic/module.ipynb b/python/basic/module.ipynb index 898c83944..380dfef3d 100644 --- a/python/basic/module.ipynb +++ b/python/basic/module.ipynb @@ -6,8 +6,9 @@ "collapsed": true }, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", - "# Training and Inference Module\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", "\n", diff --git a/python/basic/ndarray.ipynb b/python/basic/ndarray.ipynb index 25c79fa8a..11b11bfc5 100644 --- a/python/basic/ndarray.ipynb +++ b/python/basic/ndarray.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# NDArray Tutorial\n", "\n", "\n", diff --git a/python/basic/optimizer.ipynb b/python/basic/optimizer.ipynb index faf9543a5..534d90434 100644 --- a/python/basic/optimizer.ipynb +++ b/python/basic/optimizer.ipynb @@ -6,7 +6,9 @@ "collapsed": true }, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/basic/record_io.ipynb b/python/basic/record_io.ipynb index 16a4ce9f9..d78de1774 100644 --- a/python/basic/record_io.ipynb +++ b/python/basic/record_io.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/basic/symbol.ipynb b/python/basic/symbol.ipynb index 9ce21cc5b..13ee34248 100644 --- a/python/basic/symbol.ipynb +++ b/python/basic/symbol.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/cnn/mnist.ipynb b/python/cnn/mnist.ipynb index 050cdad78..451b45655 100644 --- a/python/cnn/mnist.ipynb +++ b/python/cnn/mnist.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/how_to/finetune.ipynb b/python/how_to/finetune.ipynb index 5aea75a2e..06bad315e 100644 --- a/python/how_to/finetune.ipynb +++ b/python/how_to/finetune.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/how_to/predict.ipynb b/python/how_to/predict.ipynb index fd2c28a47..220ea39f2 100644 --- a/python/how_to/predict.ipynb +++ b/python/how_to/predict.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/recommendation_systems/cdl/collaborative-dl.ipynb b/python/recommendation_systems/cdl/collaborative-dl.ipynb index ae8aa4851..e10580e18 100644 --- a/python/recommendation_systems/cdl/collaborative-dl.ipynb +++ b/python/recommendation_systems/cdl/collaborative-dl.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/rnn/lstm.ipynb b/python/rnn/lstm.ipynb index 6e2440fc0..1415d6da5 100644 --- a/python/rnn/lstm.ipynb +++ b/python/rnn/lstm.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# A Full Recipe for Recurrent LSTM Model\n", "\n", "## Introduction\n", diff --git a/python/tutorials/char_lstm.ipynb b/python/tutorials/char_lstm.ipynb index 58171c964..0150d2e4e 100644 --- a/python/tutorials/char_lstm.ipynb +++ b/python/tutorials/char_lstm.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/tutorials/linear-regression.ipynb b/python/tutorials/linear-regression.ipynb index 7ec180ee5..df16971a2 100644 --- a/python/tutorials/linear-regression.ipynb +++ b/python/tutorials/linear-regression.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# MXNet Basics - Linear Regression using MXNet" ] }, diff --git a/python/tutorials/matrix_factorization.ipynb b/python/tutorials/matrix_factorization.ipynb index b998aa39e..7a914000f 100644 --- a/python/tutorials/matrix_factorization.ipynb +++ b/python/tutorials/matrix_factorization.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/tutorials/mnist.ipynb b/python/tutorials/mnist.ipynb index 39d30696c..92d6c3658 100644 --- a/python/tutorials/mnist.ipynb +++ b/python/tutorials/mnist.ipynb @@ -6,7 +6,9 @@ "collapsed": true }, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# Handwritten Digit Recognition\n", "\n", "This tutorial guides you through a classic computer vision application: identify hand written digits with neural networks. \n", diff --git a/python/tutorials/predict_imagenet.ipynb b/python/tutorials/predict_imagenet.ipynb index fe44fb9c0..e52de9cf3 100644 --- a/python/tutorials/predict_imagenet.ipynb +++ b/python/tutorials/predict_imagenet.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\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", diff --git a/python/tutorials/understanding_vanish_gradient.ipynb b/python/tutorials/understanding_vanish_gradient.ipynb index f1612e88c..0fc117df4 100644 --- a/python/tutorials/understanding_vanish_gradient.ipynb +++ b/python/tutorials/understanding_vanish_gradient.ipynb @@ -6,7 +6,9 @@ "collapsed": true }, "source": [ - "### Download the whole repository with ‘git clone https://github.com/dmlc/mxnet-notebooks.git' and run this notebook.\n", + "### Download the whole repository with command:\n", + "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", + "### Then run this notebook.\n", "# Understanding the vanishing gradient problem through visualization" ] }, From b6969fac09bb9071fee652f78a1efd2e7c324c7e Mon Sep 17 00:00:00 2001 From: Wang Date: Wed, 15 Mar 2017 17:46:43 -0700 Subject: [PATCH 3/4] Small fix --- python/basic/module.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/python/basic/module.ipynb b/python/basic/module.ipynb index 380dfef3d..f62023121 100644 --- a/python/basic/module.ipynb +++ b/python/basic/module.ipynb @@ -9,6 +9,7 @@ "### Download the whole repository with command:\n", "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", "### Then run this notebook.\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", "\n", From 60d38ae0557be747d4856113cd7bcb596d7635fb Mon Sep 17 00:00:00 2001 From: Wang Date: Mon, 20 Mar 2017 11:25:11 -0700 Subject: [PATCH 4/4] Add session title --- python/basic/advanced_img_io.ipynb | 7 ++++--- python/basic/data.ipynb | 16 +++++++++++----- python/basic/image_io.ipynb | 7 ++++--- python/basic/initializer.ipynb | 7 ++++--- python/basic/mixed.ipynb | 7 ++++--- python/basic/module.ipynb | 7 ++++--- python/basic/ndarray.ipynb | 7 ++++--- python/basic/optimizer.ipynb | 7 ++++--- python/basic/record_io.ipynb | 7 ++++--- python/basic/symbol.ipynb | 7 ++++--- python/cnn/mnist.ipynb | 7 ++++--- python/how_to/finetune.ipynb | 7 ++++--- python/how_to/predict.ipynb | 7 ++++--- .../cdl/collaborative-dl.ipynb | 7 ++++--- python/rnn/lstm.ipynb | 7 ++++--- python/tutorials/char_lstm.ipynb | 7 ++++--- python/tutorials/linear-regression.ipynb | 7 ++++--- python/tutorials/matrix_factorization.ipynb | 7 ++++--- python/tutorials/mnist.ipynb | 7 ++++--- python/tutorials/predict_imagenet.ipynb | 7 ++++--- .../understanding_vanish_gradient.ipynb | 7 ++++--- 21 files changed, 91 insertions(+), 65 deletions(-) diff --git a/python/basic/advanced_img_io.ipynb b/python/basic/advanced_img_io.ipynb index 0dd083a16..fe27ea720 100644 --- a/python/basic/advanced_img_io.ipynb +++ b/python/basic/advanced_img_io.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/data.ipynb b/python/basic/data.ipynb index d31f42b10..4c645bcf0 100644 --- a/python/basic/data.ipynb +++ b/python/basic/data.ipynb @@ -4,9 +4,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", @@ -350,8 +355,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -365,7 +371,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.6" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/python/basic/image_io.ipynb b/python/basic/image_io.ipynb index eaa5f1cc1..45e98a7d9 100644 --- a/python/basic/image_io.ipynb +++ b/python/basic/image_io.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/initializer.ipynb b/python/basic/initializer.ipynb index 15f974af0..05382dbb2 100644 --- a/python/basic/initializer.ipynb +++ b/python/basic/initializer.ipynb @@ -6,9 +6,10 @@ "collapsed": true }, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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" diff --git a/python/basic/mixed.ipynb b/python/basic/mixed.ipynb index cef956e47..d45f29f1a 100644 --- a/python/basic/mixed.ipynb +++ b/python/basic/mixed.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/module.ipynb b/python/basic/module.ipynb index f62023121..dffe1bcca 100644 --- a/python/basic/module.ipynb +++ b/python/basic/module.ipynb @@ -6,9 +6,10 @@ "collapsed": true }, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/ndarray.ipynb b/python/basic/ndarray.ipynb index 11b11bfc5..0f60ee710 100644 --- a/python/basic/ndarray.ipynb +++ b/python/basic/ndarray.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/optimizer.ipynb b/python/basic/optimizer.ipynb index 534d90434..366e64830 100644 --- a/python/basic/optimizer.ipynb +++ b/python/basic/optimizer.ipynb @@ -6,9 +6,10 @@ "collapsed": true }, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/record_io.ipynb b/python/basic/record_io.ipynb index d78de1774..b8f4c69d9 100644 --- a/python/basic/record_io.ipynb +++ b/python/basic/record_io.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/basic/symbol.ipynb b/python/basic/symbol.ipynb index 13ee34248..61ee87d71 100644 --- a/python/basic/symbol.ipynb +++ b/python/basic/symbol.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/cnn/mnist.ipynb b/python/cnn/mnist.ipynb index 451b45655..b5a76eccb 100644 --- a/python/cnn/mnist.ipynb +++ b/python/cnn/mnist.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/how_to/finetune.ipynb b/python/how_to/finetune.ipynb index 06bad315e..2fee8b0fa 100644 --- a/python/how_to/finetune.ipynb +++ b/python/how_to/finetune.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/how_to/predict.ipynb b/python/how_to/predict.ipynb index 220ea39f2..49ebccd99 100644 --- a/python/how_to/predict.ipynb +++ b/python/how_to/predict.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/recommendation_systems/cdl/collaborative-dl.ipynb b/python/recommendation_systems/cdl/collaborative-dl.ipynb index e10580e18..253c39ea8 100644 --- a/python/recommendation_systems/cdl/collaborative-dl.ipynb +++ b/python/recommendation_systems/cdl/collaborative-dl.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/rnn/lstm.ipynb b/python/rnn/lstm.ipynb index 1415d6da5..2cececaff 100644 --- a/python/rnn/lstm.ipynb +++ b/python/rnn/lstm.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/tutorials/char_lstm.ipynb b/python/tutorials/char_lstm.ipynb index 0150d2e4e..2fe72630a 100644 --- a/python/tutorials/char_lstm.ipynb +++ b/python/tutorials/char_lstm.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/tutorials/linear-regression.ipynb b/python/tutorials/linear-regression.ipynb index df16971a2..87ac60269 100644 --- a/python/tutorials/linear-regression.ipynb +++ b/python/tutorials/linear-regression.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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" ] }, diff --git a/python/tutorials/matrix_factorization.ipynb b/python/tutorials/matrix_factorization.ipynb index 7a914000f..d39c64aa2 100644 --- a/python/tutorials/matrix_factorization.ipynb +++ b/python/tutorials/matrix_factorization.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/tutorials/mnist.ipynb b/python/tutorials/mnist.ipynb index 92d6c3658..95b7b49b6 100644 --- a/python/tutorials/mnist.ipynb +++ b/python/tutorials/mnist.ipynb @@ -6,9 +6,10 @@ "collapsed": true }, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/tutorials/predict_imagenet.ipynb b/python/tutorials/predict_imagenet.ipynb index e52de9cf3..db98516b5 100644 --- a/python/tutorials/predict_imagenet.ipynb +++ b/python/tutorials/predict_imagenet.ipynb @@ -4,9 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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", diff --git a/python/tutorials/understanding_vanish_gradient.ipynb b/python/tutorials/understanding_vanish_gradient.ipynb index 0fc117df4..1d9668e19 100644 --- a/python/tutorials/understanding_vanish_gradient.ipynb +++ b/python/tutorials/understanding_vanish_gradient.ipynb @@ -6,9 +6,10 @@ "collapsed": true }, "source": [ - "### Download the whole repository with command:\n", - "#### git clone https://github.com/dmlc/mxnet-notebooks.git\n", - "### Then run this notebook.\n", + "# 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" ] },