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实现常见的深度神经网络的学习过程，适合初学者学习使用，也可以进一步开发使用，具有较高的学习价值和学术价值。-The depth to achieve common neural network learning process, suitable for beginners to learn to use, can be further developed to use, with high learning and academic values.
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深度信念网络 (Deep Belief Network, DBN) 由 Geoffrey Hinton 在 2006 年提出。它是一种生成模型，通过训练其神经元间的权重，我们可以让整个神经网络按照最大概率来生成训练数据。我们不仅可以使用 DBN 识别特征、分类数据，还可以用它来生成数据。下面的图片展示的是用 DBN 识别手写数字： -Depth belief networks (Deep Belief Network, DBN) proposed by the Geoffrey Hinton i
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2016年6月刚刚出来的运用分形网络的深度神经网络的牛文，值得学习，欢迎大家交流。-June 2016 Niu just out of the fractal network of deep neural networks, it is worth learning, we welcome the exchange.
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通过CNN计算大气散射模型中的传播率，从而实现图像去雾-multi-scale deep neural network for single-image dehazing
by learning the mapping between hazy images and their corresponding
transmission maps
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I利用了深度学习，实现视频中的人的步态行为检测。(In this project you can find implementation of deep neural network for people identification from video by the characteristic of their gait. The processing is very robust against various covariate factors such as clothing, ca
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matlab，深度学习工具箱，经典的卷积神经网络，共有两层卷积层。输入为28*28*1的图片(Matlab, the deep learning toolbox, the classic convolution neural network, has two volumes of layers. A picture entered into 28*28*1)
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