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basf · May 25, 2024 · 280d7d5 · 280d7d5
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diff --git a/.gitignore b/.gitignore
@@ -6,4 +6,10 @@ examples_old/
 lightning_logs/
 *.egg-info
 *.ckpt
-.venv/
+<<<<<<< HEAD
+.venv/
+=======
+.venv/
+build/
+*.ipynb
+>>>>>>> documentation
diff --git a/Mamba_Tabular_Converted.jpg b/Mamba_Tabular_Converted.jpg
diff --git a/README.md b/README.md
@@ -2,6 +2,8 @@
 
 Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn's `BaseEstimator` interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and transform using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
 
+<img src="https://github.com/basf/mamba-tabular/blob/documentation/Mamba_Tabular_Converted.jpg" alt="Mamba Tabular" width="400"/>
+
 ## Features
 
 - **Comprehensive Model Suite**: Includes modules for regression (`MambularRegressor`), classification (`MambularClassifier`), and distributional regression (`MambularLSS`), catering to a wide range of tabular data tasks.
@@ -11,8 +13,9 @@ Mambular is a Python package that brings the power of Mamba architectures to tab
 - **Sklearn-like API**: The familiar scikit-learn `fit`, `predict`, and `predict_proba` methods mean minimal learning curve for those already accustomed to scikit-learn.
 - **PyTorch Lightning Under the Hood**: Built on top of PyTorch Lightning, Mambular models benefit from streamlined training processes, easy customization, and advanced features like distributed training and 16-bit precision.
 
-## Workings
-``mambular`` 
+## Documentation
+
+You can find the Mamba-Tabular API documentation [here](https://mamba-tabular.readthedocs.io/en/latest/index.html).
 
 ## Installation
 

diff --git a/docs/$READTHEDOCS_OUTPUT/html/.buildinfo b/docs/$READTHEDOCS_OUTPUT/html/.buildinfo
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