👉 Export math, often used in the context of machine learning and deep learning, is a process where model parameters (weights and biases) are saved or exported from a trained neural network for use in other applications, such as deployment on edge devices or integration into production systems. This process typically involves serializing the model's internal state, which includes its architecture, layer configurations, and parameter values. The exported parameters are then converted into a format that can be easily loaded and used in new environments, such as TensorFlow SavedModel or PyTorch's `torch.save()` function. This allows for model portability, enabling practitioners to reuse models across different platforms and devices without retraining from scratch, while maintaining performance and accuracy.
