Deep Belief Networks and deep learning

Yuming Hua, Junhai Guo, Hua Zhao
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引用次数: 108

Abstract

Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers.
深度信念网络和深度学习
深度信念网络是深度学习中的一种算法。它是解决深层神经网络学习中速度慢、过拟合现象等问题的有效方法。本文将介绍如何使用受限玻尔兹曼机来处理深度信念网络。此外,我们将Deep Belief Network与softmax分类器相结合,并将其用于手写数字的识别。
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