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Fix typos and improve clarity in installation guide #696

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Fix typos and improve clarity in installation guide #696

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c4c2e3c
Fix typo
Vruddhi18 Sep 7, 2025
6ec93b7
Fixed some grammar errors
Vruddhi18 Sep 9, 2025
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8 changes: 4 additions & 4 deletions SETUP.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Setup Guide

This document describes how to setup all the dependencies, and optionally create a virtual machine,
This document describes how to set up all the dependencies and optionally create a virtual machine,
to run the notebooks in this repository.


Expand All @@ -13,7 +13,7 @@ to run the notebooks in this repository.

## Installation

To install the repository and its dependencies follow these simple steps:
To install the repository and its dependencies follow these steps:

1. (optional) Install Anaconda with Python >= 3.6. [Miniconda](https://conda.io/miniconda.html). This step can be skipped if working on a Data Science Virtual Machine (see the compute environment section).

Expand Down Expand Up @@ -70,7 +70,7 @@ If you don't have CUDA Toolkit or don't have the right version, please download

## Compute Environments

Many computer visions scenarios are extremely computationally heavy. Training a model often requires a machine that has a strong GPU, and would otherwise be too slow.
Many computer vision scenarios are extremely computationally intensive. Training a model often requires a machine that has a strong GPU, and would otherwise be too slow.

The easiest way to get started is to use the [Azure Data Science Virtual Machine (DSVM)](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/). This VM will come installed with all the system requirements that are needed to run the notebooks in this repository. If you choose this option, you can skip the [System Requirements](#system-requirements) step in this guide as those requirements come pre-installed on the DSVM.

Expand Down Expand Up @@ -105,7 +105,7 @@ $ssh -L local_port:remote_address:remote_port <username>@<server-ip>
```

For example, if I want to run `jupyter notebook --port 8888` on my VM and I
wish to run the Jupyter notebooks on my local broswer on `localhost:9999`, I
wish to run the Jupyter notebooks on my local browser on `localhost:9999`, I
would ssh into my VM using the following command:

```
Expand Down