docs: update README

README.md

@@ -1,41 +1,28 @@
-# tsnex: Minimal t-SNEx implementation in JAX
+# TSNEx
 
-**tsnex** is a lightweight, high-performance Python library for t-Distributed Stochastic Neighbor Embedding (t-SNE) built on top of JAX. Leveraging the power of JAX, `tsnex` offers JIT compilation, automatic differentiation, and hardware acceleration support to efficiently handle high-dimensional data for visualization and clustering tasks.
+**TSNEx** is a lightweight, high-performance Python library for t-Distributed Stochastic Neighbor Embedding (t-SNE) built on top of JAX. Leveraging the power of JAX, `tsnex` offers JIT compilation, automatic differentiation, and hardware acceleration support to efficiently handle high-dimensional data for visualization and clustering tasks.
 
-[**Usage**](#usage)
-| [**Installation**](#installation)
-| [**Contributing**](#contributing)
-| [**License**](#license)
+## Installation
+Use the package manager [pip](https://pypi.org/project/tsnex/) to install `tsnex`.
+```bash
+pip install tsnex
+```
 
-## Usage<a id="usage"></a>
+## Usage
 ```python
+import tsnex
+
+# Generate some high-dimensional data
 key = jax.random.key(0)
-X = jax.random.normal(key, shape=(100, 50))
+X = jax.random.normal(key, shape=(10_000, 50))
 
+# Perform t-SNE dimensionality reduction
 X_embedded = tsnex.transform(X, n_components=2)
 ```
 
-## Installation<a id="installation"></a>
-`tsnex` can be installed using [PyPI](https://pypi.org/project/tsnex/) via `pip`:
-```
-pip install tsnex
-```
-or from GitHub directly
-```
-pip install git+git://github.com/alonfnt/tsnex.git
-```
-
-Likewise, you can clone this repository and install it locally
-
-```bash
-git clone https://github.com/alonfnt/tsnex.git
-cd tsnex
-pip install -r requirements.txt
-```
-
-## Contributing<a id="contributing"></a>
-We welcome contributions to **tsnex**! Whether it's adding new features, improving documentation, or reporting issues, please feel free to make a pull request or open an issue.
+## Contributing
+We welcome contributions to **TSNEx**! Whether it's adding new features, improving documentation, or reporting issues, please feel free to make a pull request and/or open an issue.
 
-## License<a id="license"></a>
-Bayex is licensed under the MIT License. See the ![LICENSE](LICENSE) file for more details.
+## License
+TSNEx is licensed under the MIT License. See the ![LICENSE](LICENSE) file for more details.