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the package? Don't forget to give it a GitHub ⭐ to help others find and trust it!\n\n# echoes \n\n- 🤖 Machine learning with Echo State Networks(ESN)\n- 🧱 High level API, `scikit-learn` compatible API, eg `fit-predict` interface \n- ⚡️ Fast! (Thanks to numba ✨)\n\n\n## Installation and quick start\n```bash\npip install echoes\n```\n\nSee [Documentation](https://fabridamicelli.github.io/echoes/) and [example notebooks](https://github.com/fabridamicelli/echoes/tree/master/docs/examples/notebooks).\n\n## Feedback\nAny bugs, suggestions, problems? Just [open an issue!](https://github.com/fabridamicelli/echoes/issues/new)\n\n## `echoes` in the wild\nHere's a selection of scientific publications using this package to investigate a number of scientific topics:\n\n- [Memory-augmented hybrid quantum reservoir computing](https://journals.aps.org/prapplied/abstract/10.1103/wzwv-7rk2)\n- [Brain Connectivity meets Reservoir Computing](https://doi.org/10.1371/journal.pcbi.1010639)\n- [A Computational Perspective on the No-Strong-Loops Principle in Brain Networks](https://www.biorxiv.org/content/10.1101/2025.09.24.678310v1)\n\n## Citing\nIf you find echoes useful for a publication, then please use the following BibTeX to cite it:\n\n```\n@misc{echoes,\n  author = {Damicelli, Fabrizio},\n  title = {echoes: Echo State Networks with Python},\n  year = {2019},\n  publisher = {GitHub},\n  journal = {GitHub repository},\n  howpublished = {\\url{https://github.com/fabridamicelli/echoes}},\n}\n```\n\n## Tests \nRun tests with \n```bash\nmake test\n```\n\n## References\n  - [Reservoir computing approaches to recurrent neural network training, Mantas \u0026 Jaeger, 2009](https://www.sciencedirect.com/science/article/pii/S1574013709000173)\n  - [A Practical Guide to Applying Echo State Networks, Mantas, 2012](https://link.springer.com/chapter/10.1007/978-3-642-35289-8_36)\n  - [Echo state network (scholarpedia), Jaeger](http://www.scholarpedia.org/article/Echo_state_network)\n  - [Short Term Memory in Echo State Networks, Jaeger, 2001](http://publica.fraunhofer.de/eprints/urn_nbn_de_0011-b-731310.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabridamicelli%2Fechoes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffabridamicelli%2Fechoes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabridamicelli%2Fechoes/lists"}