👉 Instead computing is an approach to machine learning that focuses on making models more interpretable, efficient, and robust by shifting the computation from the model to the data. Unlike traditional deep learning methods where parameters are learned during training, instead computing involves encoding data into a compact representation and then using this representation to make predictions. This technique reduces the complexity of models, making them easier to understand and debug, while also improving their performance on certain tasks by leveraging the inherent structure of the data. By doing so, instead computing aims to create models that are not only accurate but also transparent and efficient, particularly useful in scenarios where interpretability is crucial.
