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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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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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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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深度神经网络VGG-16模型的keras代码，用于图像识别-keras codes of deep neural network VGG-16 model, used of image classification
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深度学习，人工神经网络的模型，逐层学习算法，可以构建多层的-deep learning, artificial neural network model, learning algorithm, can construct a multi-layer
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卷积神经网络是一种特殊的深层的神经网络模型，它的特殊性体现在两个方面，一方面它的神经元间的连接是非全连接的， 另一方面同一层中某些神经元之间的连接的权重是共享的（即相同的）。它的非全连接和权值共享的网络结构使之更类似于生物 神经网络，降低了网络模型的复杂度（对于很难学习的深层结构来说，这是非常重要的），减少了权值的数量。-Convolution neural network is a kind of special deep neural network model, its particula
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使用tensorflow实现几类深度学习，如卷积神经网络、自回归神经网络、动态神经网络等-Use tensorflow to achieve several kinds of deep learning, such as convolution neural network, recurrent neural network, dynamic neural network
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世界顶级杂志《自然》，针对人工智能的深度学习进行的最全面综合论述，以及对未来深度学习及神经网络的发展预测，值得一读！-The world s top magazine nature , for the depth of artificial intelligence to learn the most comprehensive exposition, as well as the future development of deep learning and neural network p
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CNN - Convolutional neural network class This project provides matlab class for implementation of convolutional neural networks.
Deep Neural Network It provides deep learning tools of deep belief networks (DBNs).
myCNN is a Matlab implementation
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深度学习主要通过人工神经网络的思想发现数据的分布式特征表示-Deep learning discovers the distributed representation through artificial neural network.
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GoogleNet 卷积神经网络 图片分类 分类精度高 网络结构深(GoogleNet convolution neural network image classification, high classification accuracy, network structure is deep)
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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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基于tensorflow的简单神经网络代码实现(Implementation of simple neural network code based on tensorflow)
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基于tensorflow的mnist数据集卷积神经网络简单代码实现。(MNIST dataset based on tensorflow convolutional neural network simple code implementation)
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卷积神经网络、循环神经网络、递归神经网络、深度信念网络、深度堆叠网络、LSTM长短时记忆(Convolution neural network, circulating neural network, recursive neural network, deep belief network, deep stack network, LSTM length memory)
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深度学习讲义，caffe官方中文译本教程，caffe个人学习笔记，卷积神经网络结构演化介绍，CNN学习总结(deep learning lecture, caffe official chinese lecture, caffe personal learning note, the Convolutional Neural Network leaning review)
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在使用深度神经网络时我们一般推荐使用大牛的组推出的和成功的网络。如最近的google团队推出的BN-inception网络和inception-v3以及微软最新的深度残差网络ResNET。(In the use of deep neural network we generally recommend the use of cattle group launched and successful network. Such as the recent google team launched B
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neural-networks-and-deep-learning-master
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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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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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