Sadly had to remove binder support, because it is too unreliable and has been down for a month.

.binder_start

@@ -1,11 +0,0 @@
-#!/bin/bash
-
-function jupyter {
-    # Run Jupyter with xvfb-run so that it can render the CartPole
-    # environment without crashing:
-    xvfb-run -s "-screen 0 1400x900x24" /home/main/.local/bin/jupyter $@
-}
-
-function ipython {
-	jupyter $@
-}

16_reinforcement_learning.ipynb

@@ -701,7 +701,7 @@
     "\n",
     "    $ xvfb-run -s \"-screen 0 1400x900x24\" jupyter notebook\n",
     "\n",
-    "If you are running this notebook using Binder, then this has been taken care of for you. If not, and you don't want to worry about Xvfb, then you can just use the following rendering function for the Cart-Pole:"
+    "If Jupyter is running on a headless server but you don't want to worry about Xvfb, then you can just use the following rendering function for the Cart-Pole:"
    ]
   },
   {

README.md

@@ -1,8 +1,6 @@
 Machine Learning Notebooks
 ==========================
 
-[![Gitter](https://badges.gitter.im/ageron/handson-ml.svg)](https://gitter.im/ageron/handson-ml?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/ageron/handson-ml)
-
 This project aims at teaching you the fundamentals of Machine Learning in
 python. It contains the example code and solutions to the exercises in my O'Reilly book [Hands-on Machine Learning with Scikit-Learn and TensorFlow](http://shop.oreilly.com/product/0636920052289.do):
 
@@ -10,17 +8,13 @@ python. It contains the example code and solutions to the exercises in my O'Reil
 
 Simply open the [Jupyter](http://jupyter.org/) notebooks you are interested in:
 
-* using Binder (recommended): [launch binder](http://mybinder.org/repo/ageron/handson-ml)
-    * no installation needed, you can immediately experiment with the code examples
-* or using [jupyter.org's notebook viewer](http://nbviewer.jupyter.org/github/ageron/handson-ml/blob/master/index.ipynb)
+* Using [jupyter.org's notebook viewer](http://nbviewer.jupyter.org/github/ageron/handson-ml/blob/master/index.ipynb)
     * note: [github.com's notebook viewer](https://github.com/ageron/handson-ml/blob/master/index.ipynb) also works but it is slower and the math formulas are not displayed correctly
 * or by cloning this repository and running Jupyter locally
     * if you prefer this option, follow the installation instructions below.
 
 # Installation
 
-No installation is required, just click the *launch binder* button above, this creates a new VM with everything you need already preinstalled, so you'll be good to go in a just a few seconds! But if you insist, here's how to install these notebooks on your own system.
-
 Obviously, you will need [git](https://git-scm.com/) and [python](https://www.python.org/downloads/) (python 3 is recommended, but python 2 should work as well).
 
 First, clone this repository:

index.ipynb

@@ -2,7 +2,10 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "# Machine Learning Notebooks\n",
     "\n",
@@ -16,7 +19,7 @@
     "\n",
     "### To run the examples\n",
     "* **Jupyter** – These notebooks are based on Jupyter. If you just plan to read without running any code, there's really nothing more to know, just keep reading! But if you want to experiment with the code examples you need to:\n",
-    "    * open these notebooks in Jupyter. If you clicked on the \"launch binder\" button in github or followed the Installation instructions, then you are good to go. If not you will need to go back to the project [home page](https://github.com/ageron/handson-ml/) and click on \"launch binder\" or follow the installation instructions.\n",
+    "    * follow the [installation instructions](https://github.com/ageron/handson-ml/#installation),\n",
     "    * learn how to use Jupyter. Start the User Interface Tour from the Help menu.\n",
     "\n",
     "### To activate extensions\n",
@@ -54,7 +57,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": []
@@ -62,21 +67,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2+"
   },
   "nav_menu": {},
   "toc": {