Deep Learning and Its Parallelization
X. Li; G. Zhang; K. Li; W. Zheng
Abstract
In recent years, deep learning has been extensively studied as a new way to train multilayer neural networks. Deep learning is a set of algorithms in machine learning, which attempts to model high-level abstractions in input data by using multiple nonlinear transformations. Many great achievements of deep learning have been made in speech recognition, computer vision, and natural language processing. Considering that data volume increases rapidly, deep learning becomes more and more important in predictive analytics of big data. We need tens of millions of parameters and billions of samples to train a high quality and practical deep learning model. As ...
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