Data Processing Techniques in Neural Network Model Training
This article explores data processing techniques in deep learning model training, including data format standardization, loss function calculation, and model construction debugging. The article provides practical advice on topics such as input data organization, KL divergence calculation techniques, and Transformer model applications, aiming to help readers improve their model training effectiveness.
