diff --git a/doc/Homepage.ipynb b/doc/Homepage.ipynb
index 0687c0ffa8..6075ca062a 100644
--- a/doc/Homepage.ipynb
+++ b/doc/Homepage.ipynb
@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible.  Without HoloViews, there are typically many steps required before you can see your data, whether you use a GUI interactively or write a function or script to build up a plot.  HoloViews instead lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization.  For instance, if you wrap a two-dimensional dataset like the fractal below ([mandelbrot.npy](https://github.com/ioam/holoviews/raw/master/doc/mandelbrot.npy)) in a HoloViews ``Image`` object named ``fractal``,  you can just type ``fractal`` to view it as an image in an [IPython/Jupyter Notebook](http://ipython.org/notebook/).  Most importantly, combining it with other objects is now easy -- you can e.g. view it annotated with a horizontal line and a histogram, next to a slice of it from the indicated cross-section, all without writing any plotting code:"
+    "HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible.  Without HoloViews, there are typically many steps required before you can see your data, whether you use a GUI interactively or write a function or script to build up a plot.  HoloViews instead lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization.  \n",
+    "\n",
+    "For instance, if you wrap a two-dimensional dataset like the fractal below ([mandelbrot.npy](https://github.com/ioam/holoviews/raw/master/doc/mandelbrot.npy)) in a HoloViews ``Image`` object named ``fractal``,  you can just type ``fractal`` to view it as an image in an [IPython/Jupyter Notebook](http://ipython.org/notebook/).  Most importantly, combining it with other objects is now easy -- you can e.g. view it annotated with a horizontal line and a histogram, next to a slice of it from the indicated cross-section, all without writing any plotting code:"
    ]
   },
   {
@@ -17,7 +19,7 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
-    "hv.notebook_extension('matplotlib')\n",
+    "hv.notebook_extension('bokeh')\n",
     "fractal = hv.Image(np.load('mandelbrot.npy'))\n",
     "\n",
     "((fractal * hv.HLine(y=0)).hist() + fractal.sample(y=0))"
@@ -41,7 +43,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Even extremely complex relationships between data elements can be expressed succinctly in HoloViews, allowing you to explore them with ease:"
+    "Even extremely complex relationships between data elements can be expressed succinctly in HoloViews, allowing you to explore them with ease, independent of the plotting backend you choose to display them:"
    ]
   },
   {
@@ -52,10 +54,11 @@
    },
    "outputs": [],
    "source": [
+    "%%output backend='matplotlib'\n",
     "%%opts Points [scaling_factor=50] Contours (color='w')\n",
     "dots = np.linspace(-0.45, 0.45, 19)\n",
     "\n",
-    "hv.HoloMap({y: (fractal * fractal.sample([(i,y) for i in dots]).to.points(['x','y'], 'z') +\n",
+    "hv.HoloMap({y: (fractal * hv.Points(fractal.sample([(i,y) for i in dots])) +\n",
     "                fractal.sample(y=y) +\n",
     "                hv.operation.threshold(fractal, level=np.percentile(fractal.sample(y=y).data, 90)) +\n",
     "                hv.operation.contours(fractal, levels=[np.percentile(fractal.sample(y=y).data, 60)]))\n",
diff --git a/doc/mandelbrot.npy b/doc/mandelbrot.npy
index 2b292a9385..571df46f09 100644
Binary files a/doc/mandelbrot.npy and b/doc/mandelbrot.npy differ