Update

01_intro.ipynb

@@ -302,7 +302,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The software: PyTorch, fastai, and Jupyter (and why it doesn't matter)"
+    "## The software: PyTorch, fastai, and Jupyter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(and why it doesn't matter)"
    ]
   },
   {

04_mnist_basics.ipynb

@@ -1661,7 +1661,7 @@
     "- Take the mean of the *absolute value* of differences (_absolute value_ is the function that replaces negative values with positive values). This is called the *mean absolute difference* or *L1 norm*\n",
     "- Take the mean of the *square* of differences (which makes everything positive) and then take the *square root* (which *undoes* the squaring). This is called the *root mean squared error (RMSE)* or *L2 norm*.\n",
     "\n",
-    "> important: in this book we generally assume that you have completed high school maths, and remember at least some of it... But everybody forgets some things! It all depends on what you happen to have had reason to practice in the meantime. Perhaps you have forgotten what a _square root_ is, or exactly how they work. No problem! Any time you come across a maths concept that is not explained fully in this book, don't just keep moving on, but instead stop and look it up. Make sure you understand the basic idea of what that the maths concept is, how it works, and why we might be using it. One of the best places to refresh your understanding is Khan Academy. For instance, Khan Academy has a great [introduction to square roots](https://www.khanacademy.org/math/algebra/x2f8bb11595b61c86:rational-exponents-radicals/x2f8bb11595b61c86:radicals/v/understanding-square-roots)."
+    "> important: In this book we generally assume that you have completed high school maths, and remember at least some of it... But everybody forgets some things! It all depends on what you happen to have had reason to practice in the meantime. Perhaps you have forgotten what a _square root_ is, or exactly how they work. No problem! Any time you come across a maths concept that is not explained fully in this book, don't just keep moving on, but instead stop and look it up. Make sure you understand the basic idea of what that the maths concept is, how it works, and why we might be using it. One of the best places to refresh your understanding is Khan Academy. For instance, Khan Academy has a great [introduction to square roots](https://www.khanacademy.org/math/algebra/x2f8bb11595b61c86:rational-exponents-radicals/x2f8bb11595b61c86:radicals/v/understanding-square-roots)."
    ]
   },
   {

07_sizing_and_tta.ipynb

@@ -833,9 +833,10 @@
     "\n",
     "Here is how you train a model with Mixup:\n",
     "\n",
-    "```\n",
+    "```python\n",
     "model = xresnet50()\n",
-    "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy, cbs=Mixup)\n",
+    "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), \n",
+    "                metrics=accuracy, cbs=Mixup)\n",
     "learn.fit_one_cycle(5, 3e-3)\n",
     "```"
    ]
@@ -925,7 +926,8 @@
     "\n",
     "```python\n",
     "model = xresnet50()\n",
-    "learn = Learner(dls, model, loss_func=LabelSmoothingCrossEntropy(), metrics=accuracy)\n",
+    "learn = Learner(dls, model, loss_func=LabelSmoothingCrossEntropy(), \n",
+    "                metrics=accuracy)\n",
     "learn.fit_one_cycle(5, 3e-3)\n",
     "```\n",
     "\n",

13_convolutions.ipynb

@@ -2504,7 +2504,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Do you recognize that name? We saw a picture of him back in <<chapter_production>>, when we were talking about the Turing Award winners who set the foundation of deep learning today!\n",
+    "Do you recognize that name? We saw it in <<chapter_production>>, when we were talking about the Turing Award winners who set the foundation of deep learning today!\n",
     "\n",
     "Jeremy also asked on Twitter for help checking our description of label smoothing in <<chapter_sizing_and_tta>> was accurate, and got a response from again from directly from Christian Szegedy (label smoothing was originally introduced in the Inception paper):"
    ]

16_accel_sgd.ipynb

@@ -483,7 +483,7 @@
    ],
    "source": [
     "#hide_input\n",
-    "#id img_mommentum\n",
+    "#id img_momentum\n",
     "#caption An example of momentum\n",
     "#alt Graph showing an example of momentum\n",
     "x = np.linspace(-4, 4, 100)\n",

clean/01_intro.ipynb

@@ -56,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The software: PyTorch, fastai, and Jupyter (and why it doesn't matter)"
+    "## The software: PyTorch, fastai, and Jupyter"
    ]
   },
   {