v1.0.1

[manujosephv]

• Added a new task - Self Supervised Learning (SSL) and a separate training API for it.
• Added new SOTA model - Gated Additive Tree Ensembles (GATE).
• Added one SSL model - Denoising AutoEncoder.
• Added lots of new tutorials and updated entire documentation.
• Improved code documentation and type hints.
• Separated a Model into separate Embedding, Backbone, and Head.
• Refactored all models to separate Backbone as native PyTorch Model(nn.Module).
• Refactored commonly used modules (layers, activations etc. to a common module).
• Changed MixedDensityNetworks completely (breaking change). Now MDN is a head you can use with any model.
• Enabled a low level api for training model.
• Enabled saving and loading of datamodule.
• Added trainer_kwargs to pass any trainer argument PyTorch Lightning supports.
• Added Early Stopping and Model Checkpoint kwargs to use all the arguments in PyTorch Lightining.
• Enabled prediction using GPUs in predict method.
• Added reset_model to reset model weights to random.
• Added many save and load functions including ONNX(experimental).
• Added random seed as a parameter.
• Switched over completely to Rich progressbars from tqdm.
• Fixed class-balancing / mu propagation and set default to 1.0.
• Added PyTorch Profiler for debugging performance issues.
• Fixed bugs with FTTransformer and TabTransformer.
• Updated MixedDensityNetworks fixing a bug with lambda_pi.
• Many CI/CD improvements including complete integration with GitHub Actions.
• Upgraded all dependencies, including PyTorch Lightning, pandas, to latest versions and added dependabot to manage it going forward.
• Added pre-commit to ensure code integrity and standardization.

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This discussion was created from the release v1.0.1.