👉 Basis engineering is a fundamental concept in machine learning and deep learning that involves designing and selecting the foundational components or "bases" of a model to optimize its performance for specific tasks. This process includes choosing appropriate activation functions, layer architectures, and initialization methods that can significantly impact the model's ability to learn and generalize from data. By carefully engineering these basic elements, practitioners can tailor the model's capacity to capture complex patterns and relationships in data, ultimately enhancing its predictive accuracy and efficiency. This approach is particularly crucial in scenarios where the underlying data distribution is intricate or when working with limited data, as it allows for more effective utilization of the model's architecture to achieve better results.
