Few modifications to getting started doc

docs/help/gettingStartedWithAI.md

@@ -40,6 +40,7 @@ This is a high level walkthrough of some of the concepts and terms you’ll see
 - Tweak and Iterate - Remember, it’s best to change one thing at a time so you know what is working and what isn't. Sometimes you just need to try a new image, and other times using a new prompt might be the ticket. For testing, consider turning off the “random” Seed - Using the same seed with the same settings will produce the same image, which makes it the perfect way to learn exactly what your changes are doing.
 - Explore Advanced Settings - InvokeAI has a full suite of tools available to allow you complete control over your image creation process - Check out our [docs if you want to learn more](https://invoke-ai.github.io/InvokeAI/features/).
 
+
 ## Terms & Concepts
 
 If you're interested in learning more, check out [this presentation](https://docs.google.com/presentation/d/1IO78i8oEXFTZ5peuHHYkVF-Y3e2M6iM5tCnc-YBfcCM/edit?usp=sharing) from one of our maintainers (@lstein). 
@@ -60,8 +61,6 @@ Invoke offers a simple way to download several different models upon installatio
 
 - *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*
 
-### Noise
-
 ### Scheduler
 
 Schedulers guide the process of removing noise (de-noising) from data. They determine:
@@ -92,4 +91,5 @@ ControlNets are neural network models that are able to extract key features from
 
 ### VAE
 
-Variational auto-encoder (VAE) is a encode/decode model that translates the "latents" image produced during the image generation procees to the large pixel images that we see. 
+Variational auto-encoder (VAE) is a encode/decode model that translates the "latents" image produced during the image generation procees to the large pixel images that we see. 
+