Small edits (#482)

docs/getting_started/ds/best_practices.md

@@ -29,7 +29,7 @@ The goal of this phase is to get a code, dataset, and environment setup, so you
 - [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
   while also creating an easy queue interface that easily lets you just drop your experiments to be executed one by one
   (great for ensuring that the GPUs are churning during the weekend).
-- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, just like you'd develop on you local laptop!
+- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, just like you'd develop on your local laptop!
 
 ## Train Remotely
 
@@ -58,7 +58,7 @@ that we need.
 Track everything--from obscure parameters to weird metrics, it's impossible to know what will end up
 improving your results later on!
 
-- Make sure experiments are reproducible! ClearML logs code, parameters, environment in a single, easily searchable place. 
+- Make sure experiments are reproducible! ClearML logs code, parameters, and environment in a single, easily searchable place. 
 - Development is not linear. Configuration / Parameters should not be stored in your git, as
   they are temporary and constantly changing. They still need to be logged because who knows, one day...
 - Uncommitted changes to your code should be stored for later forensics in case that magic number actually saved the day. Not every line change should be committed.
@@ -72,7 +72,7 @@ everything under the same roof has its benefits!
 Being able to track experiment progress and compare experiments, and based on that send experiments to execution on remote
 machines (that also build the environment themselves) has tremendous benefits in terms of visibility and ease of integration.
 
-Being able to have visibility in your pipeline, while using experiments already defined in the platform 
+Being able to have visibility in your pipeline, while using experiments already defined in the platform, 
 enables users to have a clearer picture of the pipeline's status 
 and makes it easier to start using pipelines earlier in the process by simplifying chaining tasks.
 

docs/hyperdatasets/annotations.md

@@ -39,14 +39,14 @@ The `box2d_xywh` argument specifies the coordinates of the annotation's bounding
 a list of labels for the annotation.
 
 Enter the annotation's boundaries in one of the following ways: 
-* `poly2d_xy` - A list of floating points (x,y) to create for single polygon, or a list of floating points lists for a 
+* `poly2d_xy` - A list of floating points (x,y) to create a single polygon, or a list of floating points lists for a 
   complex polygon.
 * `ellipse2d_xyrrt` - A List consisting of cx, cy, rx, ry, and theta for an ellipse. 
 * And more! See [`SingleFrame.add_annotation`](../references/hyperdataset/singleframe.md#add_annotation) for further options. 
 
 ### Adding a Frame Label
 
-Adding a frame label is similar to creating a frame objects, except that coordinates don't need to be specified, since 
+Adding a frame label is similar to creating a frame object, except that coordinates don't need to be specified, since 
 the whole frame is being referenced. 
 
 Use the `SingleFrame.add_annotation` method, but use only the `labels` parameter. 

docs/hyperdatasets/overview.md

@@ -27,7 +27,7 @@ A Hyper-Dataset is composed of the following components:
 These components interact in a way that enables revising data and tracking and accessing all of its versions. 
 
 Frames are the basic units of data in ClearML Enterprise. SingleFrames and FrameGroups make up a Dataset version. 
-Dataset versions can be created, modified, and removed. The different version are recorded and available, 
+Dataset versions can be created, modified, and removed. The different versions are recorded and available, 
 so experiments, and their data are reproducible and traceable. 
 
 Lastly, Dataviews manage views of the dataset with queries, so the input data to an experiment can be defined from a 

docs/hyperdatasets/sources.md

@@ -67,7 +67,7 @@ is the source with the ID `front` and the other is the source with the ID `rear`
 * `meta` - Additional metadata is included for the angle of the camera (`angle`) and its field of vision (`fov`).
 
 :::note
-Sources includes a variety of content types. This example shows mp4 video.
+Sources includes a variety of content types. This example shows an mp4 video.
 :::
 
 ### Example 2: Images Sources

docusaurus.config.js

@@ -143,7 +143,7 @@ module.exports = {
               href: 'https://join.slack.com/t/clearml/shared_invite/zt-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg',
             },
             {
-              label: 'Youtube',
+              label: 'YouTube',
               href: 'https://www.youtube.com/c/ClearML',
             },
             {