diff --git a/.gitignore b/.gitignore
index 424e4bc..699ff99 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-*.h5
\ No newline at end of file
+*.h5
+.ipynb_checkpoints/
\ No newline at end of file
diff --git a/chapter-02-car-price/02-carprice.ipynb b/chapter-02-car-price/02-carprice.ipynb
index e51557a..5f75d95 100644
--- a/chapter-02-car-price/02-carprice.ipynb
+++ b/chapter-02-car-price/02-carprice.ipynb
@@ -429,7 +429,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -443,7 +443,7 @@
    "source": [
     "plt.figure(figsize=(6, 4))\n",
     "\n",
-    "sns.distplot(df.msrp, kde=False, hist_kws=dict(color='black', alpha=1))\n",
+    "sns.histplot(df.msrp, bins=40, color='black', alpha=1)\n",
     "plt.ylabel('Frequency')\n",
     "plt.xlabel('Price')\n",
     "plt.title('Distribution of prices')\n",
@@ -458,7 +458,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -472,7 +472,7 @@
    "source": [
     "plt.figure(figsize=(6, 4))\n",
     "\n",
-    "sns.distplot(df.msrp[df.msrp < 100000], kde=False, hist_kws=dict(color='black', alpha=1))\n",
+    "sns.histplot(df.msrp[df.msrp < 100000], bins=40, color='black', alpha=1)\n",
     "plt.ylabel('Frequency')\n",
     "plt.xlabel('Price')\n",
     "plt.title('Distribution of prices')\n",
@@ -487,7 +487,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -503,7 +503,7 @@
     "\n",
     "plt.figure(figsize=(6, 4))\n",
     "\n",
-    "sns.distplot(log_price, kde=False, hist_kws=dict(color='black', alpha=1))\n",
+    "sns.histplot(log_price, bins=40, color='black', alpha=1)\n",
     "plt.ylabel('Frequency')\n",
     "plt.xlabel('Log(Price + 1)')\n",
     "plt.title('Distribution of prices after log tranformation')\n",
@@ -610,7 +610,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def linear_regression(X, y):\n",
+    "def train_linear_regression(X, y):\n",
     "    ones = np.ones(X.shape[0])\n",
     "    X = np.column_stack([ones, X])\n",
     "\n",
@@ -657,7 +657,7 @@
    "outputs": [],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)"
+    "w_0, w = train_linear_regression(X_train, y_train)"
    ]
   },
   {
@@ -676,7 +676,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -690,10 +690,8 @@
    "source": [
     "plt.figure(figsize=(6, 4))\n",
     "\n",
-    "sns.distplot(y_train, label='target', kde=False,\n",
-    "             hist_kws=dict(color='#222222', alpha=0.6))\n",
-    "sns.distplot(y_pred, label='prediction', kde=False,\n",
-    "             hist_kws=dict(color='#aaaaaa', alpha=0.8))\n",
+    "sns.histplot(y_train, label='target', color='#222222', alpha=0.6, bins=40)\n",
+    "sns.histplot(y_pred, label='prediction', color='#aaaaaa', alpha=0.8, bins=40)\n",
     "\n",
     "plt.legend()\n",
     "\n",
@@ -756,7 +754,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7616530991301591"
+       "0.7616530991301601"
       ]
      },
      "execution_count": 21,
@@ -804,13 +802,13 @@
      "output_type": "stream",
      "text": [
       "train 0.5175055465840046\n",
-      "validation 0.5172055461058329\n"
+      "validation 0.5172055461058335\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)\n",
+    "w_0, w = train_linear_regression(X_train, y_train)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train', rmse(y_train, y_pred))\n",
@@ -827,7 +825,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -842,10 +840,8 @@
     "plt.figure(figsize=(6, 4))\n",
     "\n",
     "\n",
-    "sns.distplot(y_val, label='target', kde=False,\n",
-    "             hist_kws=dict(color='#222222', alpha=0.6))\n",
-    "sns.distplot(y_pred, label='prediction', kde=False,\n",
-    "             hist_kws=dict(color='#aaaaaa', alpha=0.8))\n",
+    "sns.histplot(y_val, label='target', color='#222222', alpha=0.6, bins=40)\n",
+    "sns.histplot(y_pred, label='prediction', color='#aaaaaa', alpha=0.8, bins=40)\n",
     "\n",
     "plt.legend()\n",
     "\n",
@@ -920,13 +916,13 @@
      "output_type": "stream",
      "text": [
       "train: 0.5058876515487503\n",
-      "validation: 0.5076038849555178\n"
+      "validation: 0.5076038849557035\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)\n",
+    "w_0, w = train_linear_regression(X_train, y_train)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train:', rmse(y_train, y_pred))\n",
@@ -1011,13 +1007,13 @@
      "output_type": "stream",
      "text": [
       "train: 0.4788482615078598\n",
-      "validation: 0.4748256737225879\n"
+      "validation: 0.4748256737227088\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)\n",
+    "w_0, w = train_linear_regression(X_train, y_train)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train:', rmse(y_train, y_pred))\n",
@@ -1102,13 +1098,13 @@
      "output_type": "stream",
      "text": [
       "train: 0.4745380510924004\n",
-      "validation: 0.4685879194659594\n"
+      "validation: 0.4685879194659198\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)\n",
+    "w_0, w = train_linear_regression(X_train, y_train)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train:', rmse(y_train, y_pred))\n",
@@ -1284,14 +1280,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train: 65.13125477580496\n",
-      "validation: 53.439089141342365\n"
+      "train: 1607.4898641126447\n",
+      "validation: 830.8920785817741\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression(X_train, y_train)\n",
+    "w_0, w = train_linear_regression(X_train, y_train)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train:', rmse(y_train, y_pred))\n",
@@ -1309,7 +1305,7 @@
     {
      "data": {
       "text/plain": [
-       "-2037176210554855.5"
+       "-6.947006956027172e+17"
       ]
      },
      "execution_count": 40,
@@ -1334,7 +1330,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def linear_regression_reg(X, y, r=0.0):\n",
+    "def train_linear_regression_reg(X, y, r=0.0):\n",
     "    ones = np.ones(X.shape[0])\n",
     "    X = np.column_stack([ones, X])\n",
     "\n",
@@ -1366,7 +1362,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "    0, -2037176210554855.50, 1.53, 2037176210552875.50\n",
+      "    0, -694700695602717184.00, 278.96, 694700695602667008.00\n",
       "0.001, 7.19, -0.10, 1.81\n",
       " 0.01, 7.18, -0.10, 1.81\n",
       "  0.1, 7.05, -0.10, 1.78\n",
@@ -1377,7 +1373,7 @@
    ],
    "source": [
     "for r in [0, 0.001, 0.01, 0.1, 1, 10]:\n",
-    "    w_0, w = linear_regression_reg(X_train, y_train, r=r)\n",
+    "    w_0, w = train_linear_regression_reg(X_train, y_train, r=r)\n",
     "    print('%5s, %.2f, %.2f, %.2f' % (r, w_0, w[13], w[21]))"
    ]
   },
@@ -1390,14 +1386,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train 65.13125477580496\n",
-      "val 53.439089141342365\n"
+      "train 1607.4898641126447\n",
+      "val 830.8920785817741\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression_reg(X_train, y_train, r=0)\n",
+    "w_0, w = train_linear_regression_reg(X_train, y_train, r=0)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train', rmse(y_train, y_pred))\n",
@@ -1416,14 +1412,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train 0.464312489461214\n",
-      "val 0.46023949640725603\n"
+      "train 0.46431248945738135\n",
+      "val 0.46023949632611183\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression_reg(X_train, y_train, r=0.01)\n",
+    "w_0, w = train_linear_regression_reg(X_train, y_train, r=0.01)\n",
     "\n",
     "y_pred = w_0 + X_train.dot(w)\n",
     "print('train', rmse(y_train, y_pred))\n",
@@ -1442,14 +1438,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 1e-06 0.4602250809424434\n",
-      "0.0001 0.4602254944985981\n",
-      " 0.001 0.46022676275472696\n",
-      "  0.01 0.46023949640725603\n",
-      "   0.1 0.46037006957867715\n",
-      "     1 0.46182980426576337\n",
-      "     5 0.468407962753369\n",
-      "    10 0.4757248100693066\n"
+      " 1e-06 0.4602255729429437\n",
+      "0.0001 0.4602254945347706\n",
+      " 0.001 0.46022676266043516\n",
+      "  0.01 0.46023949632611183\n",
+      "   0.1 0.46037006958137333\n",
+      "     1 0.46182980426538955\n",
+      "     5 0.46840796275338076\n",
+      "    10 0.4757248100693528\n"
      ]
     }
    ],
@@ -1458,7 +1454,7 @@
     "X_val = prepare_X(df_val)\n",
     "\n",
     "for r in [0.000001, 0.0001, 0.001, 0.01, 0.1, 1, 5, 10]:\n",
-    "    w_0, w = linear_regression_reg(X_train, y_train, r=r)\n",
+    "    w_0, w = train_linear_regression_reg(X_train, y_train, r=r)\n",
     "    y_pred = w_0 + X_val.dot(w)\n",
     "    print('%6s' %r, rmse(y_val, y_pred))"
    ]
@@ -1472,14 +1468,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "validation: 0.46023949640725603\n",
-      "test: 0.45718136802882486\n"
+      "validation: 0.46023949632611183\n",
+      "test: 0.4571813679692604\n"
      ]
     }
    ],
    "source": [
     "X_train = prepare_X(df_train)\n",
-    "w_0, w = linear_regression_reg(X_train, y_train, r=0.01)\n",
+    "w_0, w = train_linear_regression_reg(X_train, y_train, r=0.01)\n",
     "\n",
     "X_val = prepare_X(df_val)\n",
     "y_pred = w_0 + X_val.dot(w)\n",
@@ -1541,7 +1537,7 @@
     {
      "data": {
       "text/plain": [
-       "28294.135897784377"
+       "28294.135912260714"
       ]
      },
      "execution_count": 49,
@@ -1555,6 +1551,20 @@
     "suggestion = np.expm1(y_pred)\n",
     "suggestion"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1574,7 +1584,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,