diff --git a/03_classification.ipynb b/03_classification.ipynb
index d124fd2..4c82d3f 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -495,14 +495,20 @@
     "    plt.grid(True)                              # Not shown\n",
     "    plt.axis([-50000, 50000, 0, 1])             # Not shown\n",
     "\n",
-    "plt.figure(figsize=(8, 4))                      # Not shown\n",
+    "\n",
+    "\n",
+    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
+    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(8, 4))                                                                  # Not shown\n",
     "plot_precision_recall_vs_threshold(precisions, recalls, thresholds)\n",
-    "plt.plot([7813, 7813], [0., 0.9], \"r:\")         # Not shown\n",
-    "plt.plot([-50000, 7813], [0.9, 0.9], \"r:\")      # Not shown\n",
-    "plt.plot([-50000, 7813], [0.4368, 0.4368], \"r:\")# Not shown\n",
-    "plt.plot([7813], [0.9], \"ro\")                   # Not shown\n",
-    "plt.plot([7813], [0.4368], \"ro\")                # Not shown\n",
-    "save_fig(\"precision_recall_vs_threshold_plot\")  # Not shown\n",
+    "plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], \"r:\")                 # Not shown\n",
+    "plt.plot([-50000, threshold_90_precision], [0.9, 0.9], \"r:\")                                # Not shown\n",
+    "plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], \"r:\")# Not shown\n",
+    "plt.plot([threshold_90_precision], [0.9], \"ro\")                                             # Not shown\n",
+    "plt.plot([threshold_90_precision], [recall_90_precision], \"ro\")                             # Not shown\n",
+    "save_fig(\"precision_recall_vs_threshold_plot\")                                              # Not shown\n",
     "plt.show()"
    ]
   },
diff --git a/11_training_deep_neural_networks.ipynb b/11_training_deep_neural_networks.ipynb
index 067bf9a..4c4df8c 100644
--- a/11_training_deep_neural_networks.ipynb
+++ b/11_training_deep_neural_networks.ipynb
@@ -717,8 +717,8 @@
     "    keras.layers.BatchNormalization(),\n",
     "    keras.layers.Activation(\"relu\"),\n",
     "    keras.layers.Dense(100, use_bias=False),\n",
-    "    keras.layers.Activation(\"relu\"),\n",
     "    keras.layers.BatchNormalization(),\n",
+    "    keras.layers.Activation(\"relu\"),\n",
     "    keras.layers.Dense(10, activation=\"softmax\")\n",
     "])"
    ]
diff --git a/INSTALL.md b/INSTALL.md
index 25a4991..fc5bc34 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -45,7 +45,7 @@ If you're on Windows, and you want to go through chapter 18 on Reinforcement Lea
 
 
 ## Start Jupyter
-You're almost there! You just need to register the `tf2` conda environment to Jupyter. The notebooks in this project will defaut to the environment named `python3`, so it's best to register this environment using the name `python3` (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
+You're almost there! You just need to register the `tf2` conda environment to Jupyter. The notebooks in this project will default to the environment named `python3`, so it's best to register this environment using the name `python3` (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
 
     $ python3 -m ipykernel install --user --name=python3
 
diff --git a/math_linear_algebra.ipynb b/math_linear_algebra.ipynb
index e281e26..d9e968b 100644
--- a/math_linear_algebra.ipynb
+++ b/math_linear_algebra.ipynb
@@ -525,7 +525,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you might guess, dividing a vector by a scalar is equivalent to multiplying by its inverse:\n",
+    "As you might guess, dividing a vector by a scalar is equivalent to multiplying by its multiplicative inverse (reciprocal):\n",
     "\n",
     "$\\dfrac{\\textbf{u}}{\\lambda} = \\dfrac{1}{\\lambda} \\times \\textbf{u}$"
    ]
@@ -1062,7 +1062,7 @@
     "  Q_{m1} + R_{m1} & Q_{m2} + R_{m2} & Q_{m3} + R_{m3} & \\cdots & Q_{mn} + R_{mn}  \\\\\n",
     "\\end{bmatrix}$\n",
     "\n",
-    "For example, let's create a $2 \\times 3$ matric $B$ and compute $A + B$:"
+    "For example, let's create a $2 \\times 3$ matrix $B$ and compute $A + B$:"
    ]
   },
   {