completing finance example

Author: CamDavidsonPilon

Chapter5_LossFunctions/LossFunctions.ipynb

@@ -20,7 +20,7 @@
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-      "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they are losing. In fact, they consider their wins as *negative loses*. But what's interesting is *how they measure their losses.*\n",
+      "Statisticians can be a sour bunch. Instead of considering their winnings, they only measure how much they have lost. In fact, they consider their wins as *negative loses*. But what's interesting is *how they measure their losses.*\n",
       "\n",
       "For example, consider the following example:\n",
       "\n",
@@ -52,7 +52,7 @@
       "\n",
       "A negative property about the squared-error loss is that it puts a disproportionate emphasis on large outliers. This is because the loss increases quadratically, and not linearly, as the estimate moves away. That is, the penalty of being three units away is much less than being five units away, but the penalty is not much greater than being one unit away, though in both cases the magnitude of difference is the same:\n",
       "\n",
-      "$$ \\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-2 $$\n",
+      "$$ \\frac{1^2}{3^2} \\lt \\frac{3^2}{5^2}, \\;\\; \\text{although} \\;\\; 3-1 = 5-3 $$\n",
       "\n",
       "This loss function imposes that large errors are *very* bad. A more *robust* loss function that increases linearly with the difference is the *absolute-loss*\n",
       "\n",
@@ -1522,7 +1522,7 @@
      "metadata": {},
      "source": [
       "## References\n",
-      "\n",
+      "1. Antifragile: Things That Gain from Disorder. New York: Random House. 2012. ISBN 978-1-4000-6782-4.\n",
       "1.  [Tim Saliman's solution to the Dark World's Contest](http://www.timsalimans.com/observing-dark-worlds)\n",
       "2. Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail \u2014 but Some Don't. 1. Penguin Press HC, The, 2012. Print."
      ]