Add some section headers

04_training_linear_models.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Chapter 4 – Training Linear Models**"
+    "**Chapter 4 – Training Models**"
    ]
   },
   {
@@ -89,7 +89,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linear regression using the Normal Equation"
+    "# Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Normal Equation"
    ]
   },
   {
@@ -243,7 +250,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linear regression using batch gradient descent"
+    "# Gradient Descent\n",
+    "## Batch Gradient Descent"
    ]
   },
   {
@@ -330,7 +338,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Stochastic Gradient Descent"
+    "## Stochastic Gradient Descent"
    ]
   },
   {
@@ -416,7 +424,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Mini-batch gradient descent"
+    "## Mini-batch gradient descent"
    ]
   },
   {
@@ -494,7 +502,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Polynomial regression"
+    "# Polynomial Regression"
    ]
   },
   {
@@ -616,6 +624,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Learning Curves"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 35,
@@ -678,7 +693,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Regularized models"
+    "# Regularized Linear Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ridge Regression"
    ]
   },
   {
@@ -772,6 +794,13 @@
     "sgd_reg.predict([[1.5]])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lasso Regression"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 43,
@@ -803,6 +832,13 @@
     "lasso_reg.predict([[1.5]])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Elastic Net"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 45,
@@ -815,6 +851,13 @@
     "elastic_net.predict([[1.5]])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Early Stopping"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 46,
@@ -829,13 +872,6 @@
     "X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Early stopping example:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 47,
@@ -1029,7 +1065,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Logistic regression"
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Decision Boundaries"
    ]
   },
   {
@@ -1166,6 +1209,13 @@
     "log_reg.predict([[1.7], [1.5]])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Softmax Regression"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 62,