docs: update intro and readme (#24)

README.md

@@ -1,3 +1,35 @@
 # RUL Adapt
 
-This library is a collection of unsupervised domain adaption algorithms for RUL estimation.
+[![Master](https://github.com/tilman151/rul-adapt/actions/workflows/on_push.yaml/badge.svg)](https://github.com/tilman151/rul-adapt/actions/workflows/on_push.yaml)
+[![Release](https://github.com/tilman151/rul-adapt/actions/workflows/on_release.yaml/badge.svg)](https://github.com/tilman151/rul-adapt/actions/workflows/on_release.yaml)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+This library contains a collection of unsupervised domain adaption algorithms for RUL estimation.
+They are provided as [LightningModules](https://pytorch-lightning.readthedocs.io/en/stable/api/lightning.pytorch.core.LightningModule.html#lightning.pytorch.core.LightningModule) to be used in [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/latest/).
+
+Currently, five approaches are implemented, including their original hyperparameters:
+
+* **LSTM-DANN** by Da Costa et al. (2020)
+* **ADARUL** by Ragab et al. (2020)
+* **LatentAlign** by Zhang et al. (2021)
+* **TBiGRU** by Cao et al. (2021)
+* **Consistency-DANN** by Siahpour et al. (2022)
+
+This includes the following general approaches adapted for RUL estimation:
+
+* **Domain Adaption Neural Networks (DANN)** by Ganin et al. (2016)
+* **Multi-Kernel Maximum Mean Discrepancy (MMD)** by Long et al. (2015)
+
+Each approach has an example notebook which can be found in the [examples](https://github.com/tilman151/rul-adapt/tree/master/examples) folder.
+
+## Installation
+
+This library is pip-installable. Simply type:
+
+```bash
+pip install rul-adapt
+```
+
+## Contribution
+
+Contributions are always welcome. Whether you want to fix a bug, add a feature or a new approach, just open an issue and a PR.

docs/index.md

@@ -1,17 +1,35 @@
-# Welcome to MkDocs
+# RUL Adapt
 
-For full documentation visit [mkdocs.org](https://www.mkdocs.org).
+[![Master](https://github.com/tilman151/rul-adapt/actions/workflows/on_push.yaml/badge.svg)](https://github.com/tilman151/rul-adapt/actions/workflows/on_push.yaml)
+[![Release](https://github.com/tilman151/rul-adapt/actions/workflows/on_release.yaml/badge.svg)](https://github.com/tilman151/rul-adapt/actions/workflows/on_release.yaml)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
-## Commands
+This library contains a collection of unsupervised domain adaption algorithms for RUL estimation.
+They are provided as [LightningModules][lightning.pytorch.core.LightningModule] to be used in [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/latest/).
 
-* `mkdocs new [dir-name]` - Create a new project.
-* `mkdocs serve` - Start the live-reloading docs server.
-* `mkdocs build` - Build the documentation site.
-* `mkdocs -h` - Print help message and exit.
+Currently, five approaches are implemented, including their original hyperparameters:
 
-## Project layout
+* **[LSTM-DANN][rul_adapt.approach.dann]** by Da Costa et al. (2020)
+* **[ADARUL][rul_adapt.approach.adarul]** by Ragab et al. (2020)
+* **[LatentAlign][rul_adapt.approach.latent_align]** by Zhang et al. (2021)
+* **[TBiGRU][rul_adapt.approach.tbigru]** by Cao et al. (2021)
+* **[Consistency-DANN][rul_adapt.approach.consistency]** by Siahpour et al. (2022)
 
-    mkdocs.yml    # The configuration file.
-    docs/
-        index.md  # The documentation homepage.
-        ...       # Other markdown pages, images and other files.
+This includes the following general approaches adapted for RUL estimation:
+
+* **Domain Adaption Neural Networks (DANN)** by Ganin et al. (2016)
+* **Multi-Kernel Maximum Mean Discrepancy (MMD)** by Long et al. (2015)
+
+Each approach has an example notebook which can be found in the [examples](https://github.com/tilman151/rul-adapt/tree/master/examples) folder.
+
+## Installation
+
+This library is pip-installable. Simply type:
+
+```bash
+pip install rul-adapt
+```
+
+## Contribution
+
+Contributions are always welcome. Whether you want to fix a bug, add a feature or a new approach, just open an issue and a PR.