Correction in the formula for mean square error (aimacode#850)

Author: krishnaw14

neural_nets.ipynb

@@ -82,7 +82,7 @@
     "\n",
     "In both the Perceptron and the Neural Network, we are using the Backpropagation algorithm to train our weights. Basically it achieves that by propagating the errors from our last layer into our first layer, this is why it is called Backpropagation. In order to use Backpropagation, we need a cost function. This function is responsible for indicating how good our neural network is for a given example. One common cost function is the *Mean Squared Error* (MSE). This cost function has the following format:\n",
     "\n",
-    "$$MSE=\\frac{1}{2} \\sum_{i=1}^{n}(y - \\hat{y})^{2}$$\n",
+    "$$MSE=\\frac{1}{n} \\sum_{i=1}^{n}(y - \\hat{y})^{2}$$\n",
     "\n",
     "Where `n` is the number of training examples, $\\hat{y}$ is our prediction and $y$ is the correct prediction for the example.\n",
     "\n",
@@ -221,14 +221,14 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
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