cleaner readme

README.md

@@ -1,32 +1,23 @@
 # JGNN 
 
-A native Java library for graph neural networks.
-
-**Current requirements: Java 17+**
-
-## :dart: About
+[![Latest nightly release](https://jitpack.io/v/MKLab-ITI/JGNN.svg)](https://jitpack.io/#MKLab-ITI/JGNN) [![Latest stable release](https://img.shields.io/badge/JAR-1.2.0%20stable-red)](https://github.com/MKLab-ITI/JGNN/releases/latest) 
 
 Graph Neural Networks (GNNs) are getting more and more popular, for example to
 make predictions based on relational information, or to perform inference
-on small datasets. JGNN provides cross-platform implementations of this machine
-learning paradigm that do not require dedicated hardware or firmware.
+on small datasets. JGNN provides native Java implementations of this machine
+learning paradigm, and does not require dedicated hardware or firmware.
+Follow the Jitpack badge for Gradle or Maven integration.
 
 * Cross-platform
 * Lightweight
 * Optimized: data views, automatic datatypes, SIMD, parallelized batching
-* [Neuralang](tutorials/Neuralang.md) scripting language for model definition
-
-## :rocket: Setup and links
-
-Add to your dependencies the JAR file of a specific version. Download this
-from the project's [releases](https://github.com/MKLab-ITI/JGNN/releases).
-Alternatively, include the latest nightly version as a Maven or Gradle dependency 
-by following the instructions of the JitPack distribution:
+* Neuralang scripting language for model definition
 
-[![](https://jitpack.io/v/MKLab-ITI/JGNN.svg)](https://jitpack.io/#MKLab-ITI/JGNN)
+Feel free to contribute in any way, for example through the [issue tracker](https://github.com/MKLab-ITI/JGNN/issues). In addition to bug reports, 
+requests for features and clarifications are welcome.
 
 
-:cyclone: [Guidebook](https://mklab-iti.github.io/JGNN/)
+## :rocket: [Guidebook](https://mklab-iti.github.io/JGNN/)
 
 
 ## :notebook: Citation
@@ -45,24 +36,5 @@ by following the instructions of the JitPack distribution:
 }
 ```
 
-<details>
-<summary> <b>Changes since the publication's v1.0.0</b> </summary>
-
-* Introduced [Neuralang](tutorials/Neuralang.md)
-* Autosized parameteters
-* Up to 30% less memory 
-* Up to 80% less running time
-* Renamed `GCNBuilder` to `FastBuilder`
-* Neighbor attention and message passing
-* Sort pooling and graph classification
-
-</details>
-
-
-## :thumbsup: Contributing
-
-Feel free to contribute in any way, for example through the [issue tracker](https://github.com/MKLab-ITI/JGNN/issues). In addition to bug reports, 
-requests for features and clarifications are welcome.
 
-**Copyright Emmanouil Krasanakis ([email protected]), Apache license 2.0** 
-
+**Apache license 2.0. Copyright © 2024, Emmanouil Krasanakis ([email protected]).** 

docs/index.html

@@ -252,7 +252,8 @@ <h1>2. Quickstart</h1>
 	notation. Symbolic expressions are part of a scripting-like language, called Neuralang,
 	that is covered in <a href="#neuralang">section 4</a>. However, for faster onboarding we stick to
 	the <code class="language-java">FastBuilder</code> class for creating models. This class's constructor
-	creates two constants <code>A</code> and <code>h0</code> from its two arguments to. Other constants
+	creates two constants <code>A</code> and <code>h0</code> from its two arguments, respectivel holding
+	the graph's adjacency matrix and node features. Other constants
 	and input variables can be set too, but more on this later. After instantiation, we use the
 	following model builder methods to constuct a model. Some of these methods parse symbolic expressions 
 	to fastly declare machine learning components.