Data Representation


In the realms of signal processing and machine learning, the representation of data in a numerical format is crucial for analysis, processing, and modeling. Depending on the dimensionality and complexity of the data, it can be represented in different numerical forms. The most common forms of data representation include scalar values, vectors, matrices, and tensors. 



Summary & Conclusion

Data representation is crucial in domains like signal processing and machine learning. Gaining a practical understanding of how these data structures manifest in real-world engineering scenarios enables one to look beyond the terminology. Rather than being daunted by the jargon, professionals can leverage these data formats as powerful tools for both analysis and synthesis in their respective fields.