Practice and Optimization of Deep Learning Model Training

Abstract

As IT rapidly advances, deep learning, a key AI area, has achieved significant results in image recognition, NLP, and speech recognition. This research focuses on "Practice and Optimization of Deep Learning Model Training," detailing efficient methods and techniques for practical applications. This paper reviews the basic theory and evolution of deep learning, summarizes some mainstream neural network architectures and their applicable scenarios, and emphasizes the importance of data preprocessing to improve model performance. Then, the common problems such as overfitting, underfitting, gradient disappearing or explosion in the process of model training are deeply analyzed, and the problems such as loss function selection, backpropagation mechanism, optimizer configuration and hyperparameter adjustment are discussed. In the face of the above challenges, a series of efficient optimization schemes are proposed, including data enhancement techniques, transfer learning, regularization methods, adaptive learning rate adjustment, distributed training and mixed precision training, etc., which can accelerate the convergence speed of the model and enhance the final performance. In addition, some concrete cases are selected for empirical study, and the actual effect of the proposed optimization scheme is tested by comparative experiments. This paper reviews deep learning research, discusses future directions, and notes room for optimization in theoretical architecture and computational performance. It offers practical guidance for deep learning experts and a new perspective for related scholars.