Deep learning methodologies have demonstrated exceptional capabilities in capturing intricate patterns within complex datasets. This talk discusses data representation, specifically focused on the integration of deep learning architectures with sparse data representation techniques. The proposed Deep Sparse Data Representation framework aims to unravel meaningful structures in high-dimensional spaces efficiently, offering both interpretability and computational efficiency. By incorporating sparsity-inducing constraints into deep learning models, the proposed methodology encourages the extraction of essential features while inherently promoting the reduction of irrelevant or redundant information. This talk delves into the technical foundations of the framework, emphasizing the role of sparse regularization techniques such as L1 regularization in shaping the learning process. In conclusion, the proposed Deep Sparse Data Representation framework stands as a promising advancement in the realm of data representation, providing a synergistic blend of deep learning's capacity for feature extraction and sparsity-inducing techniques' ability to streamline and enhance the representation of relevant information in high-dimensional datasets.