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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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In this paper, we propose deep transfer learning for classification of Gaussian networks with
time-delayed regulations. To ensure robust signaling, most real world problems from
related domains have inherent alternate pathways that can be learned
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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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多种深度学习框架，主要包括堆栈稀疏自动编码器，深信度网络，卷积神经网络等。对于灰度图像和高维图像，展现非常强大的学习性能。-A variety of deep learning framework, including automatic stack sparse encoder, is convinced of the network, convolution neural networks. For grayscale images and high-dimensional image, s
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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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OpenPose人体姿态识别项目是美国卡耐基梅隆大学（CMU）基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人，具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用，基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景，人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is a
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