深度学习在图像识别中的应用

Application of Deep Learning in Image Recognition

深度学习作为机器学习领域的新兴分支,致力于通过训练计算机系统以模拟人类自主学习、判断和决策的能力,构建复杂的神经网络结构进行分析学习。深度学习的根本目标在于模拟人脑神经网络的功能,以解释和处理多模态数据。文章以图像识别为切入点,深入探讨深度学习在该领域中的应用优势和算法等。通过对这一领域的系统研究,能够更深入地理解深度学习在仿真人类感知与认知过程中的关键角色,为图像识别技术的不断演进提供建议。

Deep learning,as an emerging branch of machine learning,is dedicated to training computer systems to simulate human-like abilities in autonomous learning,judgment,and decision-making.It involves constructing complex neural network structures for analytical learning.The fundamental goal is to emulate the functionality of the human brain's neural network to interpret and process multimodal data.This paper takes image recognition as the starting point to discuss the application advantages and algorithms of deep learning in this field Through systematic research in this area,we gain a deeper understanding of the crucial role of deep learning in simulating human perception and cognition processes,providing profound insights for the continuous evolution of image recognition technology.