combined jupyter-colab with python tutorial, makes much more sense now

Author: kevinzakka

index.html

@@ -114,13 +114,7 @@
 
     <div class="materials-item">
       <a href="python-numpy-tutorial/">
-        Python / Numpy Tutorial
-      </a>
-    </div>
-
-    <div class="materials-item">
-      <a href="jupyter-colab-tutorial/">
-        Jupyter Notebook / Google Colab Tutorial
+        Python / Numpy Tutorial (with Jupyter and Colab)
       </a>
     </div>
 <!--

jupyter-colab-tutorial.md

@@ -4,30 +4,29 @@ title: Jupyter Notebook / Google Colab Tutorial
 permalink: /jupyter-colab-tutorial/
 ---
 
-To get some extra practice with Python, we'll be running the
-[Python Numpy Tutorial](python-numpy-tutorial.md) as a Jupyter
-notebook or [Google Colaboratory](https://research.google.com).
-
 A Jupyter notebook lets you write and execute
-Python code locally in your web browser. Jupyter notebooks
+Python code *locally* in your web browser. Jupyter notebooks
 make it very easy to tinker with code and execute it in bits
 and pieces; for this reason they are widely used in scientific
 computing.
-Google has released a flavor of Jupyter notebooks called Colaboratory
-that runs entirely in the cloud. Colab is basically Jupyter notebook on
-steroids: it's free, requires no setup, comes preinstalled with many packages,
-is easy to share with the world, and benefits from free access to hardware
-accelerators like GPUs and TPUs (with some caveats).
+Colab on the other hand is Google's flavor of
+Jupyter notebooks that is particularly suited for machine
+learning and data analysis and that runs entirely in the *cloud*.
+Colab is basically Jupyter notebook on steroids: it's free, requires no setup,
+comes preinstalled with many packages, is easy to share with the world,
+and benefits from free access to hardware accelerators like GPUs and TPUs (with some caveats).
 
-If you wish to run this tutorial in Colab, click the `Open in Colab` badge below.
+To get yourself familiar with Python and notebooks, we'll be running
+a short tutorial as a standalone Jupyter or Colab notebook. If you wish
+to use Colab, click the `Open in Colab` badge below.
 
 <div>
   <a href="https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb" target="_blank">
     <img class="colab-badge" src="/assets/badges/colab-open.svg" alt="Colab Notebook"/>
   </a>
 </div>
 
-Otherwise, if you're reading this far, we're assuming you wish to run the notebook locally. If your virtual environment was installed correctly (as per the [setup instructions]({{site.baseurl}}/setup-instructions/)), activate it, then run `pip install notebook` to install Jupyter notebook. Next, [open the notebook](https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/jupyter-notebook-tutorial.ipynb) and download it to a directory of your choice by right-clicking on the page and selecting `Save Page As`. Then `cd` to that directory and run the following in your terminal:
+If you wish to run the notebook locally make sure your virtual environment was installed correctly (as per the [setup instructions]({{site.baseurl}}/setup-instructions/)), activate it, then run `pip install notebook` to install Jupyter notebook. Next, [open the notebook](https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/jupyter-notebook-tutorial.ipynb) and download it to a directory of your choice by right-clicking on the page and selecting `Save Page As`. Then `cd` to that directory and run the following in your terminal:
 
 ```
 jupyter notebook

python-numpy-tutorial.md

@@ -1,6 +1,6 @@
 ---
 layout: page
-title: Python Numpy Tutorial
+title: Python Numpy Tutorial (with Jupyter and Colab)
 permalink: /python-numpy-tutorial/
 ---
 
@@ -18,14 +18,17 @@ help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful
 environment for scientific computing.
 
 We expect that many of you will have some experience with Python and numpy;
-for the rest of you, this section will serve as a quick crash course both on
-the Python programming language and on the use of Python for scientific
-computing.
+for the rest of you, this section will serve as a quick crash course on both
+the Python programming language and its use for scientific
+computing. We'll also introduce notebooks, which are a very convenient way
+of tinkering with Python code. Some of you may have previous knowledge in Matlab,
+in which case we also recommend the [numpy for Matlab users](http://wiki.scipy.org/NumPy_for_Matlab_Users)
+page.
 
-Some of you may have previous knowledge in Matlab, in which case we also recommend the [numpy for Matlab users](http://wiki.scipy.org/NumPy_for_Matlab_Users) page.
 
-Table of contents:
+**Table of Contents**
 
+- [Jupyter and Colab Notebooks](#jupyter-and-colab-notebooks)
 - [Python](#python)
   - [Python versions](#python-versions)
   - [Basic data types](#basic-data-types)
@@ -52,6 +55,34 @@ Table of contents:
   - [Subplots](#subplots)
   - [Images](#images)
 
+## Jupyter and Colab Notebooks
+
+A Jupyter notebook lets you write and execute
+Python code *locally* in your web browser. Jupyter notebooks
+make it very easy to tinker with code and execute it in bits
+and pieces; for this reason they are widely used in scientific
+computing.
+Colab on the other hand is Google's flavor of
+Jupyter notebooks that is particularly suited for machine
+learning and data analysis and that runs entirely in the *cloud*.
+Colab is basically Jupyter notebook on steroids: it's free, requires no setup,
+comes preinstalled with many packages, is easy to share with the world,
+and benefits from free access to hardware accelerators like GPUs and TPUs (with some caveats).
+
+**Run Tutorial in Colab (recommended)**. If you wish to run this tutorial entirely in Colab, click the `Open in Colab` badge at the very top of this page.
+
+**Run Tutorial in Jupyter Notebook**. If you wish to run the notebook locally with Jupyter, make sure your virtual environment is installed correctly (as per the [setup instructions]({{site.baseurl}}/setup-instructions/)), activate it, then run `pip install notebook` to install Jupyter notebook. Next, [open the notebook](https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/jupyter-notebook-tutorial.ipynb) and download it to a directory of your choice by right-clicking on the page and selecting `Save Page As`. Then `cd` to that directory and run `jupyter notebook`.
+
+<div class='fig figcenter'>
+  <img src='/assets/ipython-tutorial/file-browser.png'>
+</div>
+
+This should automatically launch a notebook server at `http://localhost:8888`.
+If everything worked correctly, you should see a screen like this, showing all
+available notebooks in the current directory. Click `jupyter-notebook-tutorial.ipynb`
+and follow the instructions in the notebook. Otherwise, you can continue reading the
+tutorial with code snippets below.
+
 <a name='python'></a>
 
 ## Python