gitignore stuff, background bad intro (rewrite)

thesis/Main.tex

@@ -206,7 +206,10 @@
 
 \newchapter{background}{Background and Related Work}
 
-    \todo[inline, color=green!40]{in this section we will discuss necessary background knowledge for our chosen method and the sensor data we work with. related work exists mostly from autonomous driving which does not include subter data and mostly looks at precipitation as source of degradation, we modeled after one such paper and try to adapt the same method for the domain of rescue robots, this method is a semi-supervised deep learning approach to anomaly detection which we describe in more detail in sections 2.1 and 2.2. in the last subsection 2.3 we discuss lidar sensors and the data they produce}
+    %\todo[inline, color=green!40]{in this section we will discuss necessary background knowledge for our chosen method and the sensor data we work with. related work exists mostly from autonomous driving which does not include subter data and mostly looks at precipitation as source of degradation, we modeled after one such paper and try to adapt the same method for the domain of rescue robots, this method is a semi-supervised deep learning approach to anomaly detection which we describe in more detail in sections 2.1 and 2.2. in the last subsection 2.3 we discuss lidar sensors and the data they produce}
+
+  As the domain of robotics and embedded systems often does, this thesis constitutes quite  broad interdisciplinary challenge of various fields of study. As we will see in this chapter, anomaly detection-the methodology we posed our degradation quantification problem as-has roots in statistical analysis and finds utility in many domains. As is the case for many fields of study, there has been success in incorporating learning based techniques-especially deep learning-into it to better or more efficiently solve problems anchored in interpretation of large data amounts. The very nature of anomalies often times makes their form and structure unpredictable, which lends itself to unsupervised learning techniques-ones where the training data is not assigned labels beforehand, since you cannot label what you cannot expect. These unsupervised techniques can oftentimes be improved by utilizing a small but impactful number of labeled training data, which results in semi-supervised methods. 
+  
 
     \newsection{anomaly_detection}{Anomaly Detection}
 

tools/.gitignore

@@ -4,4 +4,5 @@ tmp
 .python-version
 .envrc
 .vscode
+test