摘 要
深度学习作为人工智能领域的重要分支，在图像识别、自然语言处理等众多应用场景中取得了显著成果，但其算法理论基础仍存在诸多待深入探究之处。本文聚焦深度学习算法理论基础及其优化策略展开研究。从神经网络结构出发，分析了前馈神经网络、卷积神经网络和循环神经网络的数学模型与特性，探讨了激活函数、损失函数对模型性能的影响机制，这是研究背景与理论支撑部分。通过调整网络结构参数、优化激活函数形式以及改进损失函数定义可有效提高模型准确率、收敛速度等关键性能指标。结论是深度学习算法性能受多种因素共同作用，创新性地提出了自适应调整网络结构与参数的新方法，为深度学习算法的进一步发展提供了新思路，主要贡献在于丰富了深度学习算法理论体系并为实际应用中的模型优化提供了有力依据。
关键词：深度学习算法 神经网络结构 优化策略
Abstract
As an important branch in the field of artificial intelligence, deep learning has achieved remarkable results in many application scenarios such as image recognition and natural language processing, but there are still many problems to be explored in the theoretical basis of its algorithm. This paper focuses on the theoretical basis of deep learning algorithm and its optimization strategy. Starting from the structure of neural networks, the mathematical models and characteristics of feedforward neural networks, convolutional neural networks and recurrent neural networks are analyzed, and the mechanism of activation function and loss function affecting model performance is discussed. By adjusting the network structure parameters, optimizing the form of activation function and improving the definition of loss function, the model accuracy and convergence speed can be effectively improved. The conclusion is that the performance of deep learning algorithms is influenced by various factors. A new method of adaptive adjustment of network structure and parameters is innovatively proposed, which provides a new idea for the further development of deep learning algorithms. The main contribution is to enrich the theoretical system of deep learning algorithms and provide a strong basis for model optimization in practical applications.
Keyword:Deep Learning Algorithm Neural Network Architecture Optimization Strategy
目 录
引言 1
1.1神经网络基本原理 1
1.2反向传播机制分析 2
1.3激活函数特性研究 2
2深度学习优化策略概述 3
2.1优化算法分类 3
2.2常见优化方法 3
2.3优化目标设定 4
3深度学习模型优化技术 5
3.1正则化方法应用 5
3.2初始化策略选择 5
3.3学习率调整方案 6
4深度学习性能提升方法 6
4.1数据增强技术 6
4.2模型剪枝策略 7
4.3并行计算优化 7
结论 8
参考文献 9
致谢 10
