From f328b2a4777e74fd48bc4555d83175de5af722fc Mon Sep 17 00:00:00 2001
From: Kartik Madhira <kartikmadhira1@gmail.com>
Date: Thu, 26 Jul 2018 23:02:46 +0530
Subject: [PATCH] cleaned part one

---
 Localization/Kalmanfilter_basics.ipynb | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb
index 99aed825..b37dd130 100644
--- a/Localization/Kalmanfilter_basics.ipynb
+++ b/Localization/Kalmanfilter_basics.ipynb
@@ -4,8 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# KF Basics - Part I\n",
-    "\n"
+    "# _KF Basics - Part I_\n",
+    "----\n"
    ]
   },
   {
@@ -13,7 +13,7 @@
    "metadata": {},
    "source": [
     "##### What is the need to describe belief in terms of PDF's?\n",
-    "This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t.). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated."
+    "This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated."
    ]
   },
   {
@@ -72,7 +72,7 @@
    "metadata": {},
    "source": [
     "## Variance, Covariance and Correlation\n",
-    "\n",
+    "----\n",
     "### Variance\n",
     "Variance is the spread of the data. The mean does'nt tell much **about** the data. Therefore the variance tells us about the **story** about the data meaning the spread of the data.\n",
     "\n",
@@ -225,7 +225,7 @@
    "metadata": {},
    "source": [
     "# Gaussians \n",
-    "\n",
+    "----\n",
     "\n",
     "\n",
     "\n",
@@ -347,7 +347,7 @@
    "metadata": {},
    "source": [
     "## Gaussian Properties\n",
-    "\n",
+    "----\n",
     "**Multiplication**\n",
     "\n",
     "\n",
@@ -757,7 +757,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,