Deep learning with limited data

数据有限的深度学习

• 批准号: RGPIN-2017-05117
• 负责人: Trappenberg, Thomas
• 金额: $ 1.89万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634668
• 关键词: Deep; learning; limited; data

Deep neural networks is a machine learning technique based on a model of neuron-like elements. The "deep" refers here to networks with many layers that are capable of representing complex functions. Such models have been proven to be extremely valuable as they can learn hierarchical data representations from examples and predict previously unseen data. Such techniques enabled much of the recent progress in computer vision and pattern recognition, and the advancements of deep neural networks was made possible by the availability of large data sets (Big Data), some refinement of algorithms, and the availability of increased computational power with specialized processors such as graphical processing units (GPUs). Companies like Google, Microsoft, Amazon and Facebook, and also numerous smaller engineering firms and market analysis firms are embracing this new technology.

深度神经网络是一种基于类神经元元素模型的机器学习技术。这里的“深度”是指具有多层的网络，能够表示复杂的功能。此类模型已被证明非常有价值，因为它们可以从示例中学习分层数据表示并预测以前未见过的数据。这些技术促成了计算机视觉和模式识别方面的最新进展，而深度神经网络的进步是通过大数据集（大数据）的可用性、算法的一些改进以及图形处理单元（GPU）等专用处理器的计算能力的增强而实现的。谷歌、微软、亚马逊和 Facebook 等公司以及众多小型工程公司和市场分析公司都在采用这项新技术。