fixed

Author: lazyexcuseforausername

posts/022/four.ipynb

@@ -22,7 +22,7 @@
    "id": "fca43aee-76fc-4241-a2d9-47445349f417",
    "metadata": {},
    "source": [
-    "\n",
+    "### Chapter 7  “The Great Kernel Rope Trick”: \n",
     "Chapter 7 introduces Support Vector Machines (SVMs) as a kind of “grown-up” version of the perceptron. Instead of just finding *some* separating line between two classes, SVMs look for the one that maximizes the margin—the distance between the boundary and the closest points from each class. Ananthaswamy traces this through Vladimir Vapnik’s work on optimal separating hyperplanes and Bernhard Boser’s role at AT&T Bell Labs in actually implementing those ideas in hardware.\n",
     "\n",
     "The core intuition that stuck with me is: if your data is linearly separable, SVMs don’t just say “great, done”; they ask, “what’s the *most robust* way to separate these points so small changes won’t wreck classification?” The support vectors—the handful of points that lie on the margin—are all that really matter. Everyone else is just background. That’s such a contrast to algorithms that try to “use” every point equally; SVMs basically admit that only a few data points end up being decisive.\n",