TabNet is a novel deep learning architecture for tabular data learning that uses sequential attention to select features for decision steps, enabling interpretability and efficient learning. It outperforms other models on various datasets and provides interpretable feature attributions. TabNet also demonstrates self-supervised learning, improving performance when unlabeled data is abundant. The architecture uses raw tabular data without preprocessing and employs gradient descent-based optimization. It uses soft feature selection with controllable sparsity, allowing a single model to perform feature selection and output mapping. TabNet's design leads to superior performance with compact representations and enables both local and global interpretability. It also shows significant performance improvements through unsupervised pre-training for tabular data. TabNet is effective for both synthetic and real-world datasets, achieving high accuracy and outperforming other methods in classification and regression tasks. The model's interpretability is demonstrated through feature importance masks and its ability to highlight relevant features. TabNet's performance is robust across different datasets and hyperparameters, and it is efficient in terms of model size and training. The model's architecture is flexible and can be adapted to various tasks, making it a valuable tool for tabular data learning.TabNet is a novel deep learning architecture for tabular data learning that uses sequential attention to select features for decision steps, enabling interpretability and efficient learning. It outperforms other models on various datasets and provides interpretable feature attributions. TabNet also demonstrates self-supervised learning, improving performance when unlabeled data is abundant. The architecture uses raw tabular data without preprocessing and employs gradient descent-based optimization. It uses soft feature selection with controllable sparsity, allowing a single model to perform feature selection and output mapping. TabNet's design leads to superior performance with compact representations and enables both local and global interpretability. It also shows significant performance improvements through unsupervised pre-training for tabular data. TabNet is effective for both synthetic and real-world datasets, achieving high accuracy and outperforming other methods in classification and regression tasks. The model's interpretability is demonstrated through feature importance masks and its ability to highlight relevant features. TabNet's performance is robust across different datasets and hyperparameters, and it is efficient in terms of model size and training. The model's architecture is flexible and can be adapted to various tasks, making it a valuable tool for tabular data learning.