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超越深度学习:机器学习的可扩展方法与模型

Beyond Deep Learning: Scalable Methods and Models for Learning
作者:Oriol Vinyals 作者单位:Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 加工时间:2014-03-17 信息来源:EECS 索取原文[106 页]
关键词:机器学习;深度学习;优化算法;递归神经网络
摘 要:In my thesis I explored several techniques to improve how to eciently model signal representations and learn useful information from them. The building block of my dissertation is based on machine learning approaches to classi cation, where a (typically non-linear) function is learned from labeled examples to map from signals to some useful information (e.g. an object class present an image, or a word present in an acoustic signal).
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