Merge pull request #115 from Unity-Technologies/fix-notebook-tutorial

Fixing notebook instructions, and some other corrections

com.unity.perception/Documentation~/Tutorial/Phase1.md

 
 As the name suggests, the _**Package Manager**_ is where you can download new packages, update or remove existing ones, and access a variety of information and additional actions for each package.
 
-* **Action**: Click on the _**+**_ sign at the top-left corner of the _**Package Manager**_ window and then choose the option _**Add package frim git URL...**_. 
+* **Action**: Click on the _**+**_ sign at the top-left corner of the _**Package Manager**_ window and then choose the option _**Add package from git URL...**_. 
 * **Action**: Enter the address `com.unity.perception` and click _**Add**_.
 
 **Note:** If you would like a specific version of the package, you can append the version to the end of the url. For example `com.unity.perception@0.1.0-preview.5`. For this tutorial, **we do not need to add a version**. You can also install the package from a local clone of the Perception repository. More information on installing local packages is available [here](https://docs.unity3d.com/Manual/upm-ui-local.html).
 </p>
 
 
-Each package can come with a set of samples. As seen in the righthand panel, the Perception package includes a sample named _**Tutorial Files**_, which will be required for completing this tutorial. The sample files consist of example foreground and background objects, randomizers, shaders, and other useful elements to work with during this tutorial. **Foreground** objects are those thatthe eventual machine learning model will try to detect, and **background** objects will be placed in the background as distractors for the model.
+Each package can come with a set of samples. As seen in the righthand panel, the Perception package includes a sample named _**Tutorial Files**_, which will be required for completing this tutorial. The sample files consist of example foreground and background objects, randomizers, shaders, and other useful elements to work with during this tutorial. **Foreground** objects are those that the eventual machine learning model will try to detect, and **background** objects will be placed in the background as distractors for the model.
 
 * **Action**: In the _**Package Manager**_ window, from the list of _**Samples**_ for the Perception package, click on the _**Import into Project**_ button for the sample named _**Tutorial Files**_.
 
 <img src="Images/background_randomizer.png" width = "400"/>
 </p>
 
-* **Action**: Click on the **▷** (play) button located at top middle section of the editor to run your simulation.
+* **Action**: Click on the **▷** (play) button located at the top middle section of the editor to run your simulation.
 
 <p align="center">
 <img src="Images/play.png" width = "500"/>
 
 * **Action**: To make sure your data is properly mounted, navigate to the `data` folder. If you see the dataset's folders there, we are good to go.
 * **Action**: Navigate to the `datasetinsights/notebooks` folder and open `Perception_Statistics.ipynb`.
-* **Action**: Once in the notebook, replace the `<GUID>` in the `data_root = /data/<GUID>` line with the name of the dataset folder inside your generated data. For example, `data_root = /data/Dataseta26351bc-1b72-46c5-9e0c-d7afd6df2974`.
+* **Action**: Once in the notebook, remove the `/<GUID>` part of the `data_root = /data/<GUID>` path. Since the dataset root is already mapped to `/data`, you can use this path directly.
 
 <p align="center">
 <img src="Images/jupyter2.png"/>
 
-Each of the code blocks in this notebook can be executed by clicking on them to select them, and the clicking the _**Run**_  button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
+Each of the code blocks in this notebook can be executed by clicking on them to select them, and then clicking the _**Run**_  button at the top of the notebook. When you run a code block, an **asterisk (\*)** will be shown next to it on the left side, until the code finishes executing.
 
 Below, you can see a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome. 
 

com.unity.perception/Documentation~/Tutorial/Phase2.md

 
 * **Action**: Add `MyLightRandomizer` to the list of Randomizers in `SimulationScenario`.
 
-You will notice the the Randomizer's UI snippet contains one Parameter named `Light Intensity Parameter`. This is the same Parameter we added in the code block above. Here, you can set the sampling distribution (`Value`), `Seed`, and `Range` for this float Parameter:
+You will notice that the Randomizer's UI snippet contains one Parameter named `Light Intensity Parameter`. This is the same Parameter we added in the code block above. Here, you can set the sampling distribution (`Value`), `Seed`, and `Range` for this float Parameter:
 
 <p align="center">
 <img src="Images/light_rand_1.png" width="420"/>

com.unity.perception/Documentation~/Tutorial/Phase3.md

 * **Action**: Click _**Sign in...**_ and follow the steps in the window that opens to sign in or create an account.
 * **Action**: Sign up for a free trial of Unity Simulation [here](https://unity.com/products/unity-simulation).
 
-Unity Simulation is a cloud-based service that makes it possible for you run thousands of instances of Unity builds in order to generate massive amounts of data. The Unity Simulation service is billed on a per-usage basis, and the free trial offers up to $100 of free credit per month. In order to access the free trial, you will need to provide credit card information. **This information will be used to charge your account if you exceed the $100 monthly credit.** A list of hourly and daily rates for various computational resources is available in the page where you first register for Unity Simulation.
+Unity Simulation is a cloud-based service that makes it possible for you to run hundreds of instances of Unity builds in order to generate massive amounts of data. The Unity Simulation service is billed on a per-usage basis, and the free trial offers up to $100 of free credit per month. In order to access the free trial, you will need to provide credit card information. **This information will be used to charge your account if you exceed the $100 monthly credit.** A list of hourly and daily rates for various computational resources is available in the page where you first register for Unity Simulation.
 
 Once you have registered for a free trial, you will be taken to your Unity Simulation dashboard, where you will be able to observe your usage and billing invoices.
 
 
 ### <a name="step-4">Step 4: Analyze the Dataset using Dataset Insights</a> 
 
- In order to to download the actual data from your run, we will now use Dataset Insights again. This time though, we will utilize some of the lines that were commented in our previous use with locally generated data.
+ In order to download the actual data from your run, we will now use Dataset Insights again. This time though, we will utilize some of the lines that were commented in our previous use with locally generated data.
 
  * **Action**: Open the Dataset Insights Jupyter notebook again, using the command below: