Verlustpräzision
Verlustpräzision, also known as loss precision, is a concept in machine learning and data science that refers to the precision with which the loss function is calculated during the training of a model. The loss function is a measure of how well the model's predictions match the actual data, and its precision is crucial for the model's performance and convergence.
In the context of deep learning, particularly with neural networks, the loss function is often calculated using
Conversely, lower precision, such as single precision (32-bit) or even half precision (16-bit), can be faster and
The choice of loss precision is therefore a trade-off between computational efficiency and model accuracy. In
In recent years, there has been growing interest in mixed precision training, where different parts of the