Home/Self-Learn/Deep-Learning.md

@@ -2,25 +2,26 @@
 
 ## Goal
 
-> The goal of this section is to dig into deep-learning in a practical way. In our opinion as an university of applied Sciences the combination of theoratical an practical content is very sustainable.
+> The goal of this section is to dig into deep-learning in a practical way. In our opinion as a University of Applied Sciences the combination of theoratical an practical content is very sustainable.
 >
-> After finishing this section you should have an idea how you can use datasets to train ML/AI Models and use them!
+> After finishing this section you should have an idea how you can use datasets to train ML/AI models and use them!
 
-### Intro into Deep Learning
+## Intro into Deep Learning
 
-> In our practical session you´re going to develop your own prediction-model and hopefully hack your coffeemaschine.
+> In our practical session you're going to develop your own prediction-model and hopefully hack your coffeemachine.
 >
-> Therefore we we want you to accomplish a baisc Deep-Learning course:
+> Therefore we we want you to accomplish a basic Deep-Learning course:
 >
-> Kaggle.com is not only a great source for Datasets, it also provides good Learning Content.
+> [Kaggle.com](https://www.kaggle.com) is not only a great source for Datasets, it also provides good learning content.
 >
-> Therefore, please complete the following course: https://www.kaggle.com/learn/intro-to-deep-learningAm At the end of the 6 lessons you will receive a certificate.
+> :books: Therefore, please complete the following course: https://www.kaggle.com/learn/intro-to-deep-learningAm 
+> At the end of the 6 lessons you will receive a certificate.
 
 ### Limitations
 
-> As you can imagine a hugh Dataset, multiple layers, iterations are increasing the neccessary ressources for training the model dramatically. Thats one reason why the kaggle-course is implemented as an Notebook.
+> As you can imagine a large dataset, multiple layers and iterations are increasing the neccessary ressources for training a model dramatically. Thats one reason why the kaggle-course is implemented as a notebook.
 >
-> A notebook provides you nothing else than running your Code not on your own hardware. But the code is running on external high performance server. For our project we will setup a different approach. Keeping the Datasets and model as a lighweight solutions allows you to run the model on your own lightweight hardware.
+> A notebook provides you nothing else than running your Code not on your own hardware, but on external high performance server. For our project we will setup a different approach. Keeping the datasets and model as a lighweight solution allows you to run the model on your own lightweight hardware.
 >
 > The trend towards lightweight AI models is gaining momentum, driven by the need for deploying AI on resource-constrained devices such as smartphones and edge devices.