Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- tensorflow
+- optimizer
+- deep-learning
+- machine-learning
+- adaptive-learning-rate
+library_name: tensorflow
+---
+
+# NEAT Optimizer
+
+**NEAT (Noise-Enhanced Adaptive Training)** is a novel optimization algorithm for deep learning that combines adaptive learning rates with controlled noise injection to improve convergence and generalization.
+
+## Overview
+
+The NEAT optimizer enhances traditional adaptive optimization methods by intelligently injecting noise into the gradient updates. This approach helps:
+- Escape local minima more effectively
+- Improve generalization performance
+- Achieve faster and more stable convergence
+- Reduce overfitting on training data
+
+## Installation
+
+### From PyPI (recommended)
+```bash
+pip install neat-optimizer
+```
+
+### From Source
+```bash
+git clone https://github.com/yourusername/neat-optimizer.git
+cd neat-optimizer
+pip install -e .
+```
+
+## Quick Start
+
+```python
+import tensorflow as tf
+from neat_optimizer import NEATOptimizer
+
+# Create your model
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(128, activation='relu'),
+    tf.keras.layers.Dense(10, activation='softmax')
+])
+
+# Use NEAT optimizer
+optimizer = NEATOptimizer(
+    learning_rate=0.001,
+    noise_scale=0.01,
+    beta_1=0.9,
+    beta_2=0.999
+)
+
+# Compile and train
+model.compile(
+    optimizer=optimizer,
+    loss='sparse_categorical_crossentropy',
+    metrics=['accuracy']
+)
+
+model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))
+```
+
+## Key Features
+
+- **Adaptive Learning Rates**: Automatically adjusts learning rates per parameter
+- **Noise Injection**: Controlled stochastic perturbations for better exploration
+- **TensorFlow Integration**: Drop-in replacement for standard TensorFlow optimizers
+- **Hyperparameter Flexibility**: Customizable noise schedules and adaptation rates
+
+## Parameters
+
+- `learning_rate` (float, default=0.001): Initial learning rate
+- `noise_scale` (float, default=0.01): Scale of noise injection
+- `beta_1` (float, default=0.9): Exponential decay rate for first moment estimates
+- `beta_2` (float, default=0.999): Exponential decay rate for second moment estimates
+- `epsilon` (float, default=1e-7): Small constant for numerical stability
+- `noise_decay` (float, default=0.99): Decay rate for noise scale over time
+
+## Requirements
+
+- Python >= 3.7
+- TensorFlow >= 2.4.0
+- NumPy >= 1.19.0
+
+## Citation
+
+If you use NEAT optimizer in your research, please cite:
+
+```bibtex
+@software{neat_optimizer,
+  title={NEAT: Noise-Enhanced Adaptive Training Optimizer},
+  author={Your Name},
+  year={2025},
+  url={https://github.com/yourusername/neat-optimizer}
+}
+```
+
+## References
+
+- Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
+- Neelakantan, A., et al. (2015). Adding gradient noise improves learning for very deep networks. arXiv preprint arXiv:1511.06807.
+
+## License
+
+This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## Support
+
+For issues, questions, or feature requests, please open an issue on GitHub.