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== 概述 == 深度学习的"深度"指神经网络中层的数量。通过堆叠多个非线性变换层,网络能够学习到数据中高度复杂的模式与表示。其核心思想是'''表示学习'''——让模型自动发现对任务有用的特征,而非依赖人类专家手工设计。 深度学习的成功离不开三大要素的共同推动:大规模标注数据的积累、图形处理器(GPU)等算力的飞跃,以及反向传播等训练算法的成熟。2012 年,深度卷积网络在 ImageNet 图像识别竞赛中大幅领先传统方法,被视为深度学习时代到来的标志性事件。
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