Model-Driven Vs Data-Driven Approach

Model-Driven Vs Data-Driven Approach

It is challenging to improve the accuracy and output of deep learning models. There are two main ways to improve a model’s output: by improving the deep learning model or by improving the dataset. Developing a deep learning model is a complex process and In order to improve its performance, many challenges must be overcome. The performance of the model can be improved by adjusting hyperparameters, trying multiple algorithms, ensemble models, etc. These methods can be very challenging to use and require a lot of hardware resources and trial and error. The model-driven approach refers to improving the output of the deep learning model by focusing on the model. An alternative method is the data-driven approach.

It should be noted that both of these two approaches are necessary to improve the output of a deep learning model, but will the effort spent improving the model be worth it if we spend the time improving data? We will examine this question throughout this article.