Merge pull request #1281 from microsoft/miguel/review

Add copyright

examples/07_tutorials/KDD2020-tutorial/README.md

@@ -1,4 +1,5 @@
 # Environment setup
+
 The following setup instructions assume users work in a Linux system. The testing was performed on a Ubuntu Linux system.
 We use Conda to install packages and manage the virtual environment. Type ``` conda list ``` to check if you have conda in your machine. If not, please follow the instructions on https://conda.io/projects/conda/en/latest/user-guide/install/linux.html to install either Miniconda or Anaconda (preferred) before we proceed. 
 
@@ -8,38 +9,49 @@ We use Conda to install packages and manage the virtual environment. Type ``` co
     ```
 
 1. Navigate to the tutorial folder. The materials for the tutorial are located under the directory of `recommenders/examples/07_tutorials/KDD2020-tutorial`.
+
     ```bash
     cd recommenders/examples/07_tutorials/KDD2020-tutorial
     ```
+
 1. Download the dataset
-    1. Download the dataset for hands on experiments and unzip to data_folder:
+
+    Download the dataset for hands on experiments and unzip to data_folder:
     ```bash
     wget https://recodatasets.z20.web.core.windows.net/kdd2020/data_folder.zip
     unzip data_folder.zip -d data_folder
     ```
+
     After you unzip the file, there are two folders under data_folder, i.e. 'raw' and 'my_cached'.   'raw' folder contains original txt files from the COVID MAG dataset. 'my_cached' folder contains processed data files, if you miss some steps during the hands-on tutorial, you can make it up by copying corresponding files into experiment folders.
+
 1. Install the dependencies
+
     1. The model pre-training will use a tool for converting the original data into embeddings. Use of the tool will require `g++`. The following installs `g++` on a Linux system.
         ```bash
         sudo apt-get install g++
         ```
-    1. The Python script will be run in a conda environment where the dependencies are installed. This can be done by using the `reco_gpu_kdd.yaml` file provided in the branch subfolder with the following commands.
+    1. The Python script will be run in a conda environment where the dependencies are installed. This can be done by using the `reco_gpu_kdd.yaml` file provided in the branch subfolder with the following commands:
+
         ```bash
         conda env create -n kdd_tutorial_2020 -f reco_gpu_kdd.yaml
         conda activate kdd_tutorial_2020
         ```
 1. The tutorial will be conducated by using the Jupyter notebooks. The newly created conda kernel can be registered with the Jupyter notebook server
+
     ```bash
     python -m ipykernel install --user --name kdd_tutorial_2020 --display-name "Python (kdd tutorial)"
     ```
 
 # Tutorial notebooks/scripts
+
 After the setup, the users should be able to launch the notebooks locally with the command 
+
 ```bash
 jupyter notebook --port=8080
 ```
 Then the notebook can be spinned off in a browser at the address of `localhost:8080`.
 Alternatively, if the jupyter notebook server is on a remote server, the users can launch the jupyter notebook by using the following command.
+
 ```bash
 jupyter notebook --no-browser --ip=10.214.70.89 --port=8080
 ```

examples/07_tutorials/KDD2020-tutorial/pandas-subgraph-local-samples.ipynb

@@ -1,5 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>\n",
+    "\n",
+    "<i>Licensed under the MIT License.</i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data visualization and analysis for Microsoft Academic Graph"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

examples/07_tutorials/KDD2020-tutorial/step3_run_dkn.ipynb

@@ -363,7 +363,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "reco_gpu",
+   "display_name": "Python (reco_gpu)",
    "language": "python",
    "name": "reco_gpu"
   },
@@ -377,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.6.11"
   },
   "pycharm": {
    "stem_cell": {