Corrected Sentence

01_intro.ipynb

@@ -227,7 +227,7 @@
    "source": [
     "Harvard professor David Perkins, who wrote _Making Learning Whole_ (Jossey-Bass), has much to say about teaching. The basic idea is to teach the *whole game*. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.\n",
     "\n",
-    "Paul Lockhart, a Columbia math PhD, former Brown professor, and K-12 math teacher, imagines in the influential [essay](https://www.maa.org/external_archive/devlin/LockhartsLament.pdf) \"A Mathematician's Lament\" a nightmare world where music and art are taught the way math is taught. Children are allowed to listen to or play music until they have spent over a decade mastering music notation and theory, spending classes transposing sheet music into a different key. In art class, students study colors and applicators, but aren't allowed to actually paint until college. Sound absurd? This is how math is taught–-we require students to spend years doing rote memorization and learning dry, disconnected *fundamentals* that we claim will pay off later, long after most of them quit the subject.\n",
+    "Paul Lockhart, a Columbia math PhD, former Brown professor, and K-12 math teacher, imagines in the influential [essay](https://www.maa.org/external_archive/devlin/LockhartsLament.pdf) \"A Mathematician's Lament\" a nightmare world where music and art are taught the way math is taught. Children are not allowed to listen to or play music until they have spent over a decade mastering music notation and theory, spending classes transposing sheet music into a different key. In art class, students study colors and applicators, but aren't allowed to actually paint until college. Sound absurd? This is how math is taught–-we require students to spend years doing rote memorization and learning dry, disconnected *fundamentals* that we claim will pay off later, long after most of them quit the subject.\n",
     "\n",
     "Unfortunately, this is where many teaching resources on deep learning begin–-asking learners to follow along with the definition of the Hessian and theorems for the Taylor approximation of your loss functions, without ever giving examples of actual working code. We're not knocking calculus. We love calculus, and Sylvain has even taught it at the college level, but we don't think it's the best place to start when learning deep learning!\n",
     "\n",