人工智能驱动的视觉传感器和传感:我们在哪里,我们要去哪里
Hieu Nguyen1,2,3, Minh Vo4, John Hyatt5
1School of Electrical Engineering, International University, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
深度学习,机器学习的一个子集,使用大脑启发的神经网络进行模式识别和决策. 它的基本概念可以追溯到20世纪50年代,影响了现代人工智能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 深度学习是一种机器学习技术,其灵感来源于人类大脑的神经网络.
- 它擅长处理复杂的数据,识别模式和做出明智的决策.
- 深度学习的理论基础可以追溯到20世纪50年代.
研究的目的:
- 提供深度学习的历史概述.
- 解释深度学习的基本原则.
- 突出深度学习在当代AI中的重要性.
主要方法:
- 对机器学习和神经网络的历史研究和出版物的审查.
- 分析关键的理论发展和概念框架.
- 综合信息以说明深度学习的演变.
主要成果:
- 深度学习的起源植根于20世纪中叶早期的计算理论.
- 该领域已经显著发展,包括计算能力和数据可用性的进步.
- 深度学习现在是现代人工智能应用的基石.
结论:
- 深度学习代表了机器学习的重大进步,具有丰富的历史.
- 它模仿认知功能的能力使其成为人工智能的强大工具.
- 了解它的起源为它的当前能力和未来潜力提供了背景.
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