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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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对学习深度神经网络很有用的资料，深入浅出地对相关的知识点进行了解析，值得一阅-Very useful for learning neural network, explain profound theories in simple language to analyze the related knowledge points, worth reading
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由微软提供的深度学习资料，包括了各种深度学习算法和开源工具包的使用。-deep learning toolkit
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matlab code for Deep Neural Network
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Deep neural network Deep neural network-Deep neural networkDeep neural network
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Fast Adaptation Of Deep Neural Network Based On Discriminant Codes For Speech Recognition
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深度学习python实现，并附有MNIST上的测试程序，准确率98 以上-Deep learning learns low and high-level features large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98 on the MNIST dataset.
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MATLAB神经网络经典程序
包括多个神经网络程序、深度学习程序-MATLAB neural network program
including mutil network and deep learning program
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很好的深度学习与神经网络教程，适合深度学习的初学者-Good depth and neural network learning tutorial for beginners to learn the depth of
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用不同的方法实现了神经网络（没有用第三方库，就是用numpy等实现的，对于初学者来说是不错的深入了解神经网络的素材）(Using different methods to achieve the neural network (not using third square libraries, that is, using numpy and so on, for beginners is a good understanding of the neural network material))
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web端直接打开对战，主要是人机对战，机器人拥有9级棋力(Play Go Against a Deep Neural Network)
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Michael Nielsen 著的神经网络与深度学习、中文版电子书(neural network and deep learning)
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neural-networks-and-deep-learning-master
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随后该书分为三部分，第一部分是应用数学和机器学习基础，当初步具有上述理论基础后，才算叩开深度学习的大门。第二部分是深层网络的现代实践。第三部分是深度学习的理论研究，适用于想要执果索因、深入学习神经网络内在原理的研究人员学习。(The book is divided into three parts, the first part is the application of mathematical and machine learning based. When initially with t
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We introduce Dynamic Deep Neural Networks (D2NN),
a new type of feed-forward deep neural network that allows
selective execution. Given an input, only a subset of D2NN
neurons are executed, and the particular subset is determined
by the D2NN itse
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Python3.6实现神经网络算法，经过mnist数据集测试后表现良好，准确率约为95%-96%。
/src 为源代码
/data为mnist算集(This is a code samples for "Neural Networks and Deep Learning" using python3.)
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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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非常适合入门的一个深度学习图片分类例程！(Very suitable for beginners to learn a deep picture classification routines!)
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深度神经网络训练过程中：首先是进行初始化，根据需求设置神经网络的基本结构；然后进行前向传递（feedforward），层与层之间进行传递，求得误差；然后进行反向传播（back propogation），根据误差最小化原则，使用随机梯度下降法，对各个参数进行求导，确定下降方向，对各个参数进行更新(In the training process of deep neural network, firstly, initialization is carried out, and the basic
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Dynamic resource allocation dqn
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