Fix some minor issues in docs

docs/using/common.md

@@ -35,7 +35,7 @@ You do so by passing arguments to the `docker run` command.
 
 - `-e NB_USER=<username>` - The desired username and associated home folder.
   The default value is `jovyan`.
-  Setting `NB_USER` refits the `jovyan` default user and ensures that the desired user has the correct file permissions
+  Setting `NB_USER` redefines the `jovyan` default user and ensures that the desired user has the correct file permissions
   for the new home directory created at `/home/<username>`.
   For this option to take effect, you **must** run the container with `--user root`, set the working directory `-w "/home/<username>"`
   and set the environment variable `-e CHOWN_HOME=yes`.
@@ -90,7 +90,7 @@ You do so by passing arguments to the `docker run` command.
   While the default `umask` value should be sufficient for most use cases, you can set the `NB_UMASK` value to fit your requirements.
 
   ```{note}
-  `NB_UMASK` when set only applies to the Jupyter process itself -
+  When `NB_UMASK` is set, it only applies to the Jupyter process itself -
   you cannot use it to set a `umask` for additional files created during `run-hooks.sh`.
   For example, via `pip` or `conda`.
   If you need to set a `umask` for these, you **must** set the `umask` value for each command.
@@ -122,7 +122,7 @@ You do so by passing arguments to the `docker run` command.
 - `-e RESTARTABLE=yes` - Runs Jupyter in a loop so that quitting Jupyter does not cause the container to exit.
   This may be useful when installing extensions that require restarting Jupyter.
 - `-v /some/host/folder/for/work:/home/jovyan/work` - Mounts a host machine directory as a folder in the container.
-  This configuration is useful for preserving notebooks and other work even after the container is destroyed.
+  This configuration is useful for preserving notebooks and other work even after the container has been destroyed.
   **You must grant the within-container notebook user or group (`NB_UID` or `NB_GID`) write access to the host directory (e.g., `sudo chown 1000 /some/host/folder/for/work`).**
 - `-e JUPYTER_ENV_VARS_TO_UNSET=ADMIN_SECRET_1,ADMIN_SECRET_2` - Unsets specified environment variables in the default startup script.
   The variables are unset after the hooks have been executed but before the command provided to the startup script runs.
@@ -136,7 +136,7 @@ You do so by passing arguments to the `docker run` command.
 You can further customize the container environment by adding shell scripts (`*.sh`) to be sourced
 or executables (`chmod +x`) to be run to the paths below:
 
-- `/usr/local/bin/start-notebook.d/` - handled **before** any of the standard options noted above are applied
+- `/usr/local/bin/start-notebook.d/` - handled **before** any of the standard options noted above is applied
 - `/usr/local/bin/before-notebook.d/` - handled **after** all the standard options noted above are applied
   and ran right before the Server launches
 
@@ -246,7 +246,7 @@ This script is handy when you derive a new Dockerfile from this image and instal
 
 The default Python 3.x [Conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html) resides in `/opt/conda`.
 The `/opt/conda/bin` directory is part of the default `jovyan` user's `${PATH}`.
-That directory is also searched for binaries when run using `sudo` (`sudo my_binary` will search for `my_binary` in `/opt/conda/bin/`
+That directory is also searched for binaries when run using `sudo` (`sudo my_binary` will search for `my_binary` in `/opt/conda/bin/`).
 
 The `jovyan` user has full read/write access to the `/opt/conda` directory.
 You can use either `mamba`, `pip`, or `conda` (`mamba` is recommended) to install new packages without any additional permissions.

docs/using/faq.md

@@ -2,7 +2,7 @@
 
 ## How to persist user data
 
-There are 2 types of data, which you might want to persist.
+There are two types of data, which you might want to persist.
 
 1. If you want to persist your environment (i.e. packages installed by `mamba`, `conda`, `pip`, `apt-get`, and so on),
    then you should create an inherited image and install packages only once while building your Dockerfile.
@@ -22,7 +22,7 @@ There are 2 types of data, which you might want to persist.
    You can find [an example of using a bind mount here](./running.md#example-2).
    There is also [a mount troubleshooting section](./troubleshooting.md#permission-denied-when-mounting-volumes) if you experience any issues.
 
-## Why we do not add your favorite package
+## Why we don't add your favorite package
 
 We have lots of users with different packages they want to use.
 Adding them all is impossible, so we have several images to choose from.

docs/using/recipes.md

@@ -60,7 +60,7 @@ docker run -it --rm \
 The default version of `Python` that ships with the image may not be the version you want.
 The instructions below permit adding a conda environment with a different `Python` version and making it accessible to Jupyter.
 You may also use older images like `jupyter/base-notebook:python-3.10`.
-A list of all tags can be found [here](https://github.com/jupyter/docker-stacks/wiki)
+A list of all tags can be found [here](https://github.com/jupyter/docker-stacks/wiki).
 
 ```{literalinclude} recipe_code/custom_environment.dockerfile
 :language: docker
@@ -400,7 +400,7 @@ docker run -it --rm \
 ## Enable nbclassic-extension spellchecker for markdown (or any other nbclassic-extension)
 
 ```{note}
-This recipe only works for NBCassic with Jupyter Notebook < 7.
+This recipe only works for NBClassic with Jupyter Notebook < 7.
 It is recommended to use [jupyterlab-spellchecker](https://github.com/jupyterlab-contrib/spellchecker) in modern environments.
 ```
 

docs/using/selecting.md

@@ -244,7 +244,7 @@ It contains:
 We provide CUDA accelerated versions of the `pytorch-notebook` and `tensorflow-notebook` images.
 Prepend a CUDA prefix (versioned prefix like `cuda12-` for `pytorch-notebook` or just `cuda-` for `tensorflow-notebook`) to the image tag
 to allow PyTorch or TensorFlow operations to use compatible NVIDIA GPUs for accelerated computation.
-We only build `pytorch-notebook` for last two major versions of CUDA.
+We only build `pytorch-notebook` for the last two major versions of CUDA.
 The `tensorflow-notebook` image only supports the latest CUDA version listed in the [officially tested build configurations](https://www.tensorflow.org/install/source#gpu).
 
 For example, you could use the image `quay.io/jupyter/pytorch-notebook:cuda12-python-3.11.8` or `quay.io/jupyter/tensorflow-notebook:cuda-latest`.

docs/using/troubleshooting.md

@@ -77,7 +77,7 @@ The following sections cover a few of these scenarios and how to fix them.
       - If you are mounting your volume inside the `/home/` directory, you can use the `-e CHOWN_HOME=yes` and `CHOWN_HOME_OPTS="-R"` flags
       instead of the `-e CHOWN_EXTRA` and `-e CHOWN_EXTRA_OPTS` in the example above.
       - This solution should work in most cases where you have created a docker volume
-      (i.e. using the [`docker volume create --name <my-volume>`command](https://docs.docker.com/engine/storage/volumes/#create-and-manage-volumes)) and mounted it using the `-v` flag in `docker run`.
+      (i.e. using the [`docker volume create --name <my-volume>` command](https://docs.docker.com/engine/storage/volumes/#create-and-manage-volumes)) and mounted it using the `-v` flag in `docker run`.
    ```
 
 2. **Matching the container's UID/GID with the host's**
@@ -108,7 +108,7 @@ The following sections cover a few of these scenarios and how to fix them.
 
    where:
 
-   - `NB_IUD` and `NB_GID` should match the local user's UID and GID.
+   - `NB_UID` and `NB_GID` should match the local user's UID and GID.
    - You **must** use `--user root` to ensure that the `UID` and `GID` are updated at runtime.
 
 ````{admonition} Additional notes
@@ -273,7 +273,7 @@ conda config --show default_channels
 You can install packages from other conda channels (e.g. `bioconda`) by disabling the `channel_priority` setting:
 
 ```bash
-# install by disabling channel priority at еру command level
+# install by disabling channel priority at the command level
 conda install --no-channel-priority -c bioconda bioconductor-geoquery
 ```