-- added custom sampler option in fit

-- added utility functions for imbalanced classification
-- added future roadmap to github readme

README.md

@@ -110,6 +110,16 @@ loaded_model = TabularModel.load_from_checkpoint("examples/basic")
 
 [PyTorch Tabular – A Framework for Deep Learning for Tabular Data](https://deep-and-shallow.com/2021/01/27/pytorch-tabular-a-framework-for-deep-learning-for-tabular-data/)
 
+## Future Roadmap(Contributions are Welcome)
+
+1. Add GaussRank as Feature Transformation
+2. Add ability to use custom activations in CategoryEmbeddingModel
+3. Add differential dropouts(layer-wise) in CategoryEmbeddingModel
+4. Add Fourier Encoding for cyclic time variables
+5. Integrate Optuna Hyperparameter Tuning
+6. Add Text and Image Modalities for mixed modal problems
+7. Integrate Wide and Deep model
+8. Integrate TabTransformer
 ## References and Citations
 
 [1] Sergei Popov, Stanislav Morozov, Artem Babenko. [*"Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data"*](https://arxiv.org/abs/1909.06312). arXiv:1909.06312 [cs.LG] (2019)

docs/.tmp/exp_version_manager.yml

@@ -1 +1 @@
-classification: 2
+classification: 17

docs/history.md

@@ -6,9 +6,11 @@ History
 
 -   First release on PyPI.
 
-0.2.0.dev0 (2021-02-03)
+0.2.0 (2021-02-07)
 ------------------
 
 -   Fixed an issue with torch.clip and torch version
 -   Fixed an issue with `gpus` parameter in TrainerConfig, by setting default value to `None` for CPU
+-   Added feature to use custom sampler in the training dataloader
+-   Updated documentation and added a new tutorial for imbalanced classification