diff --git a/docs/source/demo.ipynb b/docs/source/demo.ipynb
index 8a8e0d8..dc360fe 100644
--- a/docs/source/demo.ipynb
+++ b/docs/source/demo.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Library Examples - ChainoPy Vs. PyDTMC"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialize a Dummy TPM for Chainopy Implementation and PyDTMC"
+    "### Initialization "
    ]
   },
   {
@@ -133,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now let's compare the Runtime"
+    "### Runtime Comparision"
    ]
   },
   {
@@ -196,14 +203,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ChainoPy is approx 5x Faster than PyDTMC"
+    "#### ChainoPy is approx 5x Faster than PyDTMC"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Now let's run a simulation using my implementation"
+    "### Simulation"
    ]
   },
   {
@@ -292,7 +299,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also use my Implementation to calculate distribution of the chain after `n` steps"
+    "### N-Step Distribution"
    ]
   },
   {
@@ -321,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also save the Model as a JSON file "
+    "### Model Saving"
    ]
   },
   {
@@ -337,7 +344,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### If the matrix is TPM is sparse, with 40% or more elements equal/less than 0.0001, it will save the matrix in sparse format"
+    "### Sparisty & Model Saving"
    ]
   },
   {
@@ -396,7 +403,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also load the saved models as a new MarkovChain Object"
+    "### Load Saved Models"
    ]
   },
   {
@@ -464,7 +471,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also load a model containing TPM as a Sparse COO Matrix"
+    "### Load Sparse Models"
    ]
   },
   {
@@ -532,7 +539,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now we'll look at \"How you can transform a Markov Chain to an equivalent Neural Network using ChainoPy's MarkovChainNeuralNetwork Class "
+    "### Transform a Markov Chain to a Neural Network "
    ]
   },
   {
@@ -4685,14 +4692,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### NOTE:  A better Accuracy can be achieved using Complex Hyperparameter - Search Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let's see How can we use this library for Stock Predictions using Markov Switching Models"
+    "### Stock Predictions using Markov Switching Models"
    ]
   },
   {
@@ -4906,13 +4906,6 @@
     "ax.set_title(\"Data and Prediction\")\n",
     "ax.legend()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 295bbf9..138a5d6 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -6,15 +6,6 @@ Welcome to Chainopy's documentation!
 
    _autosummary
 
-Examples
---------
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Notebooks
-
-   examples/demo.ipynb
-
 Autosummary
 -----------
 
@@ -25,6 +16,15 @@ Autosummary
    chainopy.markov_switching
    chainopy.nn
 
+Examples
+--------
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Notebooks
+
+   demo.ipynb
+
 Readme
 ------
 
diff --git a/examples/demo.ipynb b/examples/demo.ipynb
index 8a8e0d8..dc360fe 100644
--- a/examples/demo.ipynb
+++ b/examples/demo.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Library Examples - ChainoPy Vs. PyDTMC"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Initialize a Dummy TPM for Chainopy Implementation and PyDTMC"
+    "### Initialization "
    ]
   },
   {
@@ -133,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now let's compare the Runtime"
+    "### Runtime Comparision"
    ]
   },
   {
@@ -196,14 +203,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### ChainoPy is approx 5x Faster than PyDTMC"
+    "#### ChainoPy is approx 5x Faster than PyDTMC"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Now let's run a simulation using my implementation"
+    "### Simulation"
    ]
   },
   {
@@ -292,7 +299,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also use my Implementation to calculate distribution of the chain after `n` steps"
+    "### N-Step Distribution"
    ]
   },
   {
@@ -321,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also save the Model as a JSON file "
+    "### Model Saving"
    ]
   },
   {
@@ -337,7 +344,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### If the matrix is TPM is sparse, with 40% or more elements equal/less than 0.0001, it will save the matrix in sparse format"
+    "### Sparisty & Model Saving"
    ]
   },
   {
@@ -396,7 +403,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also load the saved models as a new MarkovChain Object"
+    "### Load Saved Models"
    ]
   },
   {
@@ -464,7 +471,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### You can also load a model containing TPM as a Sparse COO Matrix"
+    "### Load Sparse Models"
    ]
   },
   {
@@ -532,7 +539,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Now we'll look at \"How you can transform a Markov Chain to an equivalent Neural Network using ChainoPy's MarkovChainNeuralNetwork Class "
+    "### Transform a Markov Chain to a Neural Network "
    ]
   },
   {
@@ -4685,14 +4692,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### NOTE:  A better Accuracy can be achieved using Complex Hyperparameter - Search Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now let's see How can we use this library for Stock Predictions using Markov Switching Models"
+    "### Stock Predictions using Markov Switching Models"
    ]
   },
   {
@@ -4906,13 +4906,6 @@
     "ax.set_title(\"Data and Prediction\")\n",
     "ax.legend()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {