Remove redundant comment

02_end_to_end_machine_learning_project.ipynb

@@ -201,8 +201,8 @@
     "plt.rc('font', size=14)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
     "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)\n",
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)\n",
     "\n",
     "housing.hist(bins=50, figsize=(12, 8))\n",
     "save_fig(\"attribute_histogram_plots\")  # not in the book\n",
@@ -1196,7 +1196,7 @@
    "outputs": [],
    "source": [
     "# not in the book – this code generates Figure 2–17\n",
-    "fig, axs = plt.subplots(1, 2, figsize=(8,3), sharey=True)\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n",
     "housing[\"population\"].hist(ax=axs[0], bins=50)\n",
     "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n",
     "axs[0].set_xlabel(\"Population\")\n",
@@ -1270,7 +1270,7 @@
     "ax2 = ax1.twinx()  # create a twin axis that shares the same x-axis\n",
     "color = \"blue\"\n",
     "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n",
-    "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\",)\n",
+    "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n",
     "ax2.tick_params(axis='y', labelcolor=color)\n",
     "ax2.set_ylabel(\"Age similarity\", color=color)\n",
     "\n",

03_classification.ipynb

@@ -89,8 +89,8 @@
     "plt.rc('font', size=14)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
     "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)"
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
    ]
   },
   {
@@ -1471,7 +1471,7 @@
     "shifted_image_down = shift_image(image, 0, 5)\n",
     "shifted_image_left = shift_image(image, -5, 0)\n",
     "\n",
-    "plt.figure(figsize=(12,3))\n",
+    "plt.figure(figsize=(12, 3))\n",
     "plt.subplot(131)\n",
     "plt.title(\"Original\")\n",
     "plt.imshow(image.reshape(28, 28),\n",
@@ -2045,7 +2045,7 @@
     "plt.figure(figsize=(8, 4))\n",
     "plt.plot([1]*10, svm_scores, \".\")\n",
     "plt.plot([2]*10, forest_scores, \".\")\n",
-    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\",\"Random Forest\"))\n",
+    "plt.boxplot([svm_scores, forest_scores], labels=(\"SVM\", \"Random Forest\"))\n",
     "plt.ylabel(\"Accuracy\")\n",
     "plt.show()"
    ]
@@ -2321,7 +2321,7 @@
    "outputs": [],
    "source": [
     "for header, value in spam_emails[0].items():\n",
-    "    print(header,\":\",value)"
+    "    print(header, \":\", value)"
    ]
   },
   {

05_support_vector_machines.ipynb

@@ -91,8 +91,8 @@
     "plt.rc('font', size=14)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
     "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)"
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
    ]
   },
   {
@@ -185,7 +185,7 @@
     "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#AAA',\n",
     "                zorder=-1)\n",
     "\n",
-    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
     "\n",
     "plt.sca(axes[0])\n",
     "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
@@ -231,7 +231,7 @@
     "X_scaled = scaler.fit_transform(Xs)\n",
     "svm_clf_scaled = SVC(kernel=\"linear\", C=100).fit(X_scaled, ys)\n",
     "\n",
-    "plt.figure(figsize=(9,2.7))\n",
+    "plt.figure(figsize=(9, 2.7))\n",
     "plt.subplot(121)\n",
     "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
     "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
@@ -281,7 +281,7 @@
     "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
     "svm_clf2.fit(Xo2, yo2)\n",
     "\n",
-    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
     "\n",
     "plt.sca(axes[0])\n",
     "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
@@ -393,7 +393,7 @@
     "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
     "svm_clf2.support_vectors_ = X[support_vectors_idx2]\n",
     "\n",
-    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
     "\n",
     "plt.sca(axes[0])\n",
     "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris virginica\")\n",
@@ -1121,7 +1121,7 @@
     "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
     "sgd_clf.C = C\n",
     "\n",
-    "plt.figure(figsize=(5.5,3.2))\n",
+    "plt.figure(figsize=(5.5, 3.2))\n",
     "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
     "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
     "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",

06_decision_trees.ipynb

@@ -91,8 +91,8 @@
     "plt.rc('font', size=14)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
     "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)"
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
    ]
   },
   {
@@ -215,7 +215,7 @@
     "\n",
     "# not in the book – just formatting details\n",
     "from matplotlib.colors import ListedColormap\n",
-    "custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
+    "custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
     "plt.figure(figsize=(8, 4))\n",
     "\n",
     "lengths, widths = np.meshgrid(np.linspace(0, 7.2, 100), np.linspace(0, 3, 100))\n",

07_ensemble_learning_and_random_forests.ipynb

@@ -91,8 +91,8 @@
     "plt.rc('font', size=14)\n",
     "plt.rc('axes', labelsize=14, titlesize=14)\n",
     "plt.rc('legend', fontsize=14)\n",
-    "plt.rc('xtick',labelsize=10)\n",
-    "plt.rc('ytick',labelsize=10)"
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)"
    ]
   },
   {
@@ -144,7 +144,7 @@
     "cumulative_heads = coin_tosses.cumsum(axis=0)\n",
     "cumulative_heads_ratio = cumulative_heads / np.arange(1, 10001).reshape(-1, 1)\n",
     "\n",
-    "plt.figure(figsize=(8,3.5))\n",
+    "plt.figure(figsize=(8, 3.5))\n",
     "plt.plot(cumulative_heads_ratio)\n",
     "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n",
     "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n",
@@ -484,7 +484,7 @@
     "\n",
     "m = len(X_train)\n",
     "\n",
-    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
+    "fix, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
     "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n",
     "    sample_weights = np.ones(m) / m\n",
     "    plt.sca(axes[subplot])\n",
@@ -628,7 +628,7 @@
     "        plt.legend(loc=\"upper center\")\n",
     "    plt.axis(axes)\n",
     "\n",
-    "plt.figure(figsize=(11,11))\n",
+    "plt.figure(figsize=(11, 11))\n",
     "\n",
     "plt.subplot(3, 2, 1)\n",
     "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.2, 0.8], style=\"g-\",\n",
@@ -717,7 +717,7 @@
    "source": [
     "# not in the book – this cell generates and saves Figure 7–10\n",
     "\n",
-    "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
+    "fix, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
     "\n",
     "plt.sca(axes[0])\n",
     "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\",\n",

10_neural_nets_with_keras.ipynb

@@ -138,13 +138,6 @@
     "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},