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.