fixed typos and minor reword

docs/contributing.rst

@@ -2,36 +2,40 @@
 Contributing
 ============
 
-All contributions are welcome!
+We welcome all contributions!
 
 Bug Reporting
 -------------
 
-If you find a bug, submit a bug report on GitHub Issues. 
+If you encounter a bug, please report it by creating an issue on GitHub.
 
-Adding Features/Fixing Bugs
----------------------------
+Adding Features or Fixing Bugs
+------------------------------
 
-If you have identified a new feature or bug that you can fix yourself, please follow the following procedure.
+If you’ve identified a new feature to add or a bug you can fix, follow these steps:
 
-#. Clone ``main`` branch.
-#. Create a new branch to contain your changes. 
-#. ``add``, ``commit``, and ``push`` your changes to this branch. 
-#. Create a pull request (PR). See more information on submitting a PR request below.
+#. Clone the ``main`` branch.
+#. Create a new branch to work on your changes.
+#. Use ``add``, ``commit``, and ``push`` to save your changes to the new branch.
+#. Create a pull request (PR). See the "Submitting a Pull Request" section for more details.
 
 Submitting a Pull Request
 -------------------------
 
-#. If necessary, please **write your own unit tests** and add them to `the tests directory <https://github.com/rctn/sparsecoding/blob/main/tests>`_. 
-#. Verify that all tests are passed by running `python -m unittest discover tests -vvv`.
-#. Be sure that your PR follows formatting guidelines, `PEP8 <https://peps.python.org/pep-0008/>`_ and `flake8 <https://flake8.pycqa.org/en/latest/>`_. 
-#. Make sure the title of your PR summarizes the features/issues resolved in your branch. 
-#. Submit your pull request and add reviewers. 
+#. If applicable, write unit tests for your changes and add them to the 
+   `tests directory <https://github.com/rctn/sparsecoding/blob/main/tests>`_.
+#. Verify that all tests pass by running ``python -m unittest discover tests -vvv``.
+#. Ensure your code adheres to the formatting guidelines specified in 
+   `PEP8 <https://peps.python.org/pep-0008/>`_ and validated by 
+   `flake8 <https://flake8.pycqa.org/en/latest/>`_.
+#. Provide a concise and descriptive title for your PR that summarizes the changes made in your branch.
+#. Submit your PR and assign reviewers as necessary.
 
 Coding Style Guidelines
------------------------
-We adhere to the `NumPy documentation standards <https://numpydoc.readthedocs.io/en/latest/format.html>`_.
+------------------------
 
-#. Format code in accordance with `flake8 <https://flake8.pycqa.org/en/latest/>`_ standard.
-#. Use underscores to separate words in non-class names: ``n_samples`` rather than ``nsamples``.
-#. Avoid single-character variable names.
+We follow the `NumPy documentation standards <https://numpydoc.readthedocs.io/en/latest/format.html>`_.
+
+1. Format your code according to the `flake8 <https://flake8.pycqa.org/en/latest/>`_ standard.
+2. Use underscores to separate words in non-class names (e.g., ``n_samples`` instead of ``nsamples``).
+3. Avoid single-character variable names.

docs/install.rst

@@ -2,26 +2,19 @@
 Installation
 ============
 
-The fastest way to install is by cloning the library from Github:
+The quickest way to install the library is by cloning it directly from GitHub:
 
 .. code:: bash
 
-    $ git clone https://github.com/rctn/sparsecoding.git
-    $ cd sparsecoding
-    $ pip install -e sparsecoding
-    $ pip install -r requirements.txt
+    git clone https://github.com/rctn/sparsecoding.git
+    cd sparsecoding
+    pip install -e sparsecoding
+    pip install -r requirements.txt
 
+The last command installs the dependencies required for the RCTN sparse coding library, including:
 
-The last line of the code above will install the RCTN sparse coding dependencies:
-
-- ``numpy`` 
-
+- ``numpy``
 - ``scipy``
-
 - ``matplotlib``
-
 - ``torch``
-
 - ``torchvision``
-
-

docs/quickstart.rst

@@ -7,42 +7,34 @@ Overview
 
 .. _sparsecoding: https://github.com/rctn/sparsecoding/
 
-`sparsecoding`_ is a python package that provides tools for using sparse coding algorithms. 
-Historically, sparse coding has been largely focused on learning sparse representations 
-of images and we provide visualization and transformation tools to work with such data.
-However, we've tried to structure the dictionary learning methods and inference methods 
-in a manner that is data-agnostic.
+`sparsecoding`_ is a Python package that provides tools for implementing sparse coding algorithms. 
+Traditionally, sparse coding has been primarily used for learning sparse representations of images. 
+To support this, we include tools for visualization and data transformation specific to image data. 
+However, we have designed the dictionary learning and inference methods to be data-agnostic, 
+allowing for broader applications.
 
-The `sparsecoding`_ library is largely built using PyTorch which enables it to inheret 
-many performance benifits. These include:
+The `sparsecoding`_ library is built largely on PyTorch, enabling it to inherit several 
+performance benefits, such as:
 
 - GPU support
-
 - Batched operations
-
 - Auto-grad optimizers
 
+Structure of the Library
+-------------------------
 
+The functionalities of `sparsecoding`_ are organized into several modules:
 
-Structure of library
---------------------
-
-The functionalities of `sparsecoding`_ are broken into differnent modules.
-
-- ``sparsecoding.models`` contains dictionary learning models (e.g. SparseCoding).
-
-- ``sparsecoding.inference`` contains algorithms for computing latent coefficients.
-
-- ``sparsecoding.visualization`` contains tools for visualizing image dictionaries and data.
-
-- ``sparsecoding.priors`` contains methods for sampling from various sparse coding priors.
-
-- ``sparsecoding.datasets`` contains methods for loading datasets.
-
-- ``sparsecoding.transforms`` contains methods working with data, such as whitening and extracting patches from images.
-
+- ``sparsecoding.models``: Contains dictionary learning models (e.g., SparseCoding).
+- ``sparsecoding.inference``: Includes algorithms for computing latent coefficients.
+- ``sparsecoding.visualization``: Provides tools for visualizing image dictionaries and data.
+- ``sparsecoding.priors``: Offers methods for sampling from various sparse coding priors.
+- ``sparsecoding.datasets``: Contains utilities for loading datasets.
+- ``sparsecoding.transforms``: Includes methods for working with data, such as whitening and 
+  extracting patches from images.
 
-Getting started
+Getting Started
 ---------------
 
-`See example notebooks <https://github.com/rctn/sparsecoding/tree/main/examples>`_.
+Explore our `example notebooks <https://github.com/rctn/sparsecoding/tree/main/examples>`_ 
+to get started.