Update in Readme

README.md

@@ -1,127 +1,107 @@
-<p align="center" dir="auto">
-    <a href="https://auto-differentiation.github.io" rel="nofollow" target="_blank">
-        <img src="https://auto-differentiation.github.io/images/logo.svg" height="80" alt="XAD" style="max-width:100%">
-    </a>
+<p align="center">
+  <a href="https://auto-differentiation.github.io" target="_blank">
+    <img src="https://auto-differentiation.github.io/images/logo.svg" height="80" alt="XAD">
+  </a>
 </p>
 
-XAD is a comprehensive library for automatic differentiation, available for both Python and  C++.
-It targets production-quality code at any scale, striving for both ease of use and high performance.
+# 🚀 XAD: Powerful Automatic Differentiation for C++ & Python
+
+**XAD** is the ultimate solution for automatic differentiation, combining **ease of use** with **high performance**.
+It's designed to help you differentiate complex applications with speed and precision—whether
+you're optimizing neural networks, solving scientific problems, or performing financial risk analysis.
 
 <p align="center" dir="auto">
     <a href="https://github.com/auto-differentiation/xad/releases/latest">
         <img src="https://img.shields.io/github/v/release/auto-differentiation/xad?label=Download&sort=semver" alt="Download" style="max-width: 100%;">
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-    <a href="https://github.com/auto-differentiation/xad/blob/main/LICENSE.md">
-        <img src="https://img.shields.io/github/license/auto-differentiation/xad?label=License" alt="License" style="max-width: 100%;">
-    </a>
-    <a href="https://doi.org/10.5281/zenodo.12764574">
-        <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.12764574.svg" alt="DOI" style="max-width: 100%;">
-    </a>
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+        <img src="https://img.shields.io/codacy/grade/1826d0a6c8ce4feb81ef3b482d65c7b4?logo=codacy&label=Quality%20%28Codacy%29" alt="Codacy Quality" style="max-width: 100%;">
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 </p>
 
-Automatic differentiation (also called algorithmic differentiation) is a set of techniques for calculating partial derivatives
-of functions specified as computer programs. 
-Since every program execution is always composed of a sequence of simple operations with known derivatives (arithmetics and mathematical functions like sin, exp, log, etc.),
-the chain rule can be applied repeatedly to calculate partial derivatives automatically.
-XAD implements this using operator-overloading in C++ and exposes bindings for Python,
-allowing to compute derivatives with minimal changes to the program.
-See [automatic differentiation mathematical background](https://auto-differentiation.github.io/tutorials/aad/) for more details.
-
-Application areas:
-
--   Machine Learning and Deep Learning: Training neural networks or other 
-    machine learning models.
--   Optimisation: Solving optimisation problems in engineering and finance.
--   Numerical Analysis: Enhancing numerical solution methods for differential
-    equations.
--   Scientific Computing: Simulating physical systems and processes.
--   Risk Management and Quantitative Finance: Assessing and hedging risk in
-    financial models.
--   Computer Graphics: Optimising rendering algorithms.
--   Robotics: Improving control and simulation of robotic systems.
--   Meteorology: Enhancing weather prediction models.
--   Biotechnology: Modeling biological processes and systems.
-
-Key features:
-
--   Forward and adjoint mode for any order, using operator-overloading
--   Checkpointing support (for tape memory management)
--   External functions interface (to integrate external libraries)
--   Thread-safe tape
--   Formal exception-safety guarantees
--   High performance
--   Battle-tested in large production code bases
-
-## Getting Started
-
-XAD in C++ builds with modern CMake and has no external dependencies. 
-For instructions how to build and integrate it into your projects, please refer to the
-[Installation Guide](https://auto-differentiation.github.io/installation/).
-
-The documentation site also contains [tutorials](https://auto-differentiation.github.io/tutorials/), 
-[examples](https://auto-differentiation.github.io/examples/), 
-and information about [integrating XAD into QuantLib](https://auto-differentiation.github.io/quantlib-risks/cxx/).
+## 🌟 Why XAD?
 
-The sources for the reference section of the site are located in the [docs](docs) directory in this repository.
+XAD is trusted by professionals for its **speed**, **flexibility**, and **scalability** across various fields:
 
-## Getting Help
+- **Machine Learning & Deep Learning**: Accelerate neural network training and model optimization.
+- **Optimization in Engineering & Finance**: Solve complex problems with high precision.
+- **Numerical Analysis**: Improve methods for solving differential equations efficiently.
+- **Scientific Computing**: Simulate physical systems and processes with precision.
+- **Risk Management & Quantitative Finance**: Assess and hedge risks in sophisticated financial models.
+- **Computer Graphics**: Optimize rendering algorithms for high-quality graphics.
+- **Robotics**: Enhance control and simulation for robotic systems.
+- **Meteorology**: Improve accuracy in weather prediction models.
+- **Biotechnology**: Model complex biological processes effectively.
 
-If you have found an issue, want to report a bug, or have a feature request, please raise a [GitHub issue](https://github.com/auto-differentiation/xad/issues).
+### Key Features
 
-For general questions about XAD, sharing ideas, engaging with community members, etc, please use [GitHub Discussions](https://github.com/auto-differentiation/xad/discussions).
+- **Forward & Adjoint Mode**: Supports any order using operator overloading.
+- **Checkpointing Support**: Efficient tape memory management for large-scale applications.
+- **External Function Interface**: Seamlessly connect with external libraries.
+- **Thread-Safe Tape**: Ensure safe, concurrent operations.
+- **Exception-Safe**: Formal guarantees for stability and error handling.
+- **High Performance**: Optimized for speed and efficiency.
+- **Proven in Production**: Battle-tested in large-scale, mission-critical systems.
 
-## Related Projects
+## 💻 Example
 
-- XAD Comprehensive automatic differentiation in [Python](https://github.com/auto-differentiation/xad-py) and [C++](https://github.com/auto-differentiation/xad)
-- QuantLib-Risks: Fast risk evaluations in [Python](https://github.com/auto-differentiation/QuantLib-Risks-Py) and [C++](https://github.com/auto-differentiation/QuantLib-Risks-Cpp)
+Calculate first-order derivatives of an arbitrary function with two inputs and one output using XAD in adjoint mode.
 
-## Planned Features
+```c++
+Adouble x0 = 1.3;              // initialise inputs
+Adouble x1 = 5.2;  
+tape.registerInput(x0);        // register independent variables
+tape.registerInput(x1);        // with the tape
+tape.newRecording();           // start recording derivatives
+Adouble y = func(x0, x1);      // run main function
+tape.registerOutput(y);        // register the output variable
+derivative(y) = 1.0;           // seed output adjoint to 1.0
+tape.computeAdjoints();        // roll back adjoints to inputs
+cout << "dy/dx0=" << derivative(x0) << "\n"
+     << "dy/dx1=" << derivative(x1) << "\n";
+```
 
-Please see the [issues list](https://github.com/auto-differentiation/xad/issues?q=is%3Aopen+is%3Aissue+label%3Aenhancement) for planned/open features.
-You are very welcome to contribute towards these (or other) features - please contact the project maintainers before to discuss you proposal.
-If you have new feature requests, please submit a new issue with a detailed description.
+## 🚀 Getting Started
 
-## Contributing
+```bash
+git clone https://github.com/auto-differentiation/xad.git
+cd xad
+mkdir build
+cd build
+cmake ..
+make
+```
 
-Please read [CONTRIBUTING](CONTRIBUTING.md) for the process of contributing to this project.
-Please also obey our [Code of Conduct](CODE_OF_CONDUCT.md) in all communication.
+For more detailed guides,
+refer to our [**Installation Guide**](https://auto-differentiation.github.io/installation/)
+and explore [**Tutorials**](https://auto-differentiation.github.io/tutorials/).
 
-## Versioning
+## 🤝 Contributing
 
-We use [SemVer](http://semver.org/) for versioning,
-making a new release available as soon as sufficient new features have been merged into `main`.
-The final decision about a release and which features are included is with the project maintainers.
-For the versions available, see the [releases in GitHub](https://github.com/auto-differentiation/xad/releases).
+Want to get involved? We welcome contributors from all backgrounds! Check out
+our [**Contributing Guide**](CONTRIBUTING.md) and join the conversation in our
+[**Discussions**](https://github.com/auto-differentiation/xad/discussions).
 
-Every new version is also added to the [Changelog](CHANGELOG.md),
-which needs to be maintained throughout the development.
-That is, every pull request should also update the Changelog accordingly.
+## 🐛 Found a Bug?
 
-## Authors
+Please report any issues through our
+[**Issue Tracker**](https://github.com/auto-differentiation/xad/issues).
 
--   Various contributors from Xcelerit
--   See also the list of [contributors](https://github.com/auto-differentiation/xad/contributors) who participated in the project.
+---
 
+## 📦 Related Projects
 
-## License
+- [XAD-Py](https://github.com/auto-differentiation/xad-py): XAD in Python.
+- [QuantLib-Risks](https://github.com/auto-differentiation/QuantLib-Risks-Cpp): Fast
+  risk evaluations in C++ and Python.
 
-This project is licensed under the GNU Affero General Public License - see the [LICENSE.md](LICENSE.md) file for details.
+---