Update README.

Author: sdl1

README.ipynb

@@ -6,7 +6,9 @@
    "source": [
     "# Nabla\n",
     "\n",
-    "Automatic, machine-precision forward differentiation in python using dual numbers."
+    "Automatic, machine-precision forward differentiation in python using dual numbers.\n",
+    "\n",
+    "[Example use for logistic regression](examples/mnist/logistic.ipynb)"
    ]
   },
   {
@@ -35,7 +37,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Support for multiple variables:"
+    "## Support for multiple variables"
    ]
   },
   {
@@ -68,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Specify variables explicitly by position:"
+    "## Specify variables explicitly by position"
    ]
   },
   {
@@ -93,19 +95,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use decorators; interop with numpy:"
+    "## Use decorators; interop with numpy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dual(-0.35017548837401463,  [-0.2248451 -0.7651474])\n"
+      "Dual(-0.35017548837401463,  [-0.2248451 -0.7651474])\n",
+      "[Dual(-0.35017548837401463,  [-0.2248451 -0.        -0.        -0.7651474])\n",
+      " Dual(-0.37840124765396416,  [ 0.          0.17317819  0.         -0.82682181])\n",
+      " Dual(-0.05872664492762098,  [ 0.          0.          0.41198225 -0.12832006])]\n"
      ]
     }
    ],
@@ -116,7 +121,10 @@
     "def f(x, y):\n",
     "    return sin(x)*cos(y)\n",
     "\n",
-    "print(f(1,2))"
+    "print(f(1,2))\n",
+    "\n",
+    "# nabla can automatically differentiate w.r.t. a combination of numpy array entries and other function arguments: \n",
+    "print(f(np.array([1,2,3]), 2))"
    ]
   },
   {

README.md

@@ -3,6 +3,8 @@
 
 Automatic, machine-precision forward differentiation in python using dual numbers.
 
+[Example use for logistic regression](examples/mnist/logistic.ipynb)
+
 
 ```python
 from nabla import grad
@@ -16,7 +18,7 @@ print(grad(f)(5))
     Dual(75,  [30.])
 
 
-Support for multiple variables:
+## Support for multiple variables
 
 
 ```python
@@ -35,7 +37,7 @@ print(grad(f)(x, y, param, z))
     Dual(85,  [  4.   2. 108.])
 
 
-Specify variables explicitly by position:
+## Specify variables explicitly by position
 
 
 ```python
@@ -46,7 +48,7 @@ print(grad([1,0])(f)(x, y, param, z))
     Dual(85,  [2. 4.])
 
 
-Use decorators; interop with numpy:
+## Use decorators; interop with numpy
 
 
 ```python
@@ -57,9 +59,15 @@ def f(x, y):
     return sin(x)*cos(y)
 
 print(f(1,2))
+
+# nabla can automatically differentiate w.r.t. a combination of numpy array entries and other function arguments: 
+print(f(np.array([1,2,3]), 2))
 ```
 
     Dual(-0.35017548837401463,  [-0.2248451 -0.7651474])
+    [Dual(-0.35017548837401463,  [-0.2248451 -0.        -0.        -0.7651474])
+     Dual(-0.37840124765396416,  [ 0.          0.17317819  0.         -0.82682181])
+     Dual(-0.05872664492762098,  [ 0.          0.          0.41198225 -0.12832006])]
 
 
 ## Gradient descent without any extra code