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卷积神经网络的Csharp版本，已经编译成windowsAPI库，根据Mike的源代码改写而成。供研究卷积神经网络CNN的人使用。-Csharp version convolutional neural network has been compiled into windowsAPI library, according to the source code adapted from Mike. CNN convolutional neural networks for research of
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CNN Class, ver 0.72.
Change log:
Ver 0.72:
Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition.
Training runs 20 faster.
Ver 0.71:
Bug fix: training was stoped after 1 epoch.
Ver 0.70:
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This project provides matlab class for implementation of convolutional neural networks. This networks was created by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot na
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用于书写体数字识别的卷积神经网络，VC++源码，仅供学习参考-Convolutional Neural Networks in Handwritten Digit Recognition Visual C++ implemented.
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端至端卷积神经网络的文字识别,代码演示包.
它包含我们的论文中使用的所有主要组成部分: kmeans无监督特征学习 + 卷积神经网络(CNN)-This is a demo package of the code we used for our paper, "End-to-End
Text Recognition with Convolutional Neural Networks", T. Wang, D. Wu,
A. Coates, A. Ng, in ICPR 2012.
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深度学习（Deep learning）matlab工具包-`NN/` - A library for Feedforward Backpropagation Neural Networks
`CNN/` - A library for Convolutional Neural Networks
`DBN/` - A library for Deep Belief Networks
`SAE/` - A library for Stacked Auto-
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CNN卷积神经网络 手写体识别 c#实现源代码。很好很经典。-Csharp version convolutional neural network . according to the source code adapted from Mike. CNN convolutional neural networks for research of the people.
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CNN卷积神经网络C++实现。完全可编译运行。对研究CNN卷积神经网络 很有帮助。-C++ version convolutional neural network。 according to the source code adapted from Mike. CNN convolutional neural networks for research of the people.
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This project provides matlab class for implementation of convolutional neural networks. This networks was developed by Yann LeCun and have sucessfully used in many practical applications, such as handwritten digits recognition, face detection, robot
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Convolutional Neural Networks for No-Reference Image Quality Assessment(泛读)
Domain Transform for Edge-Aware Image and Video Processing
NVIDIA Update: CUDA Week in Review (Spotlight on Deep Neural Networks CUDA 6)
Skilling up for Oracle Fusion
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深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。[1]
深度学习的概念由Hinton等人于2006年提出。基于深信度网(DBN)提出非监督贪心逐层训练算法，为解决深层结构相关的优化难题带来希望，随后提出多层自动编码器深层结构。此外Lecun等人提出的卷积神经网络是第一个真正多层结构学习算法，它利用空间相对关系减少参数数目以提高训练性能。[1]
深度学习是机器
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Learning to Compare Image Patches via Convolutional Neural Networks
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DeepLearnToolbox_CNN_lzbV2.0
深度学习,卷积神经网络,Matlab工具箱
参考文献:
[1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006
[2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998
[3] https://github.com/rasmusberg
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CNN - 主程序
参考文献:
[1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006
[2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998
[3] https://github.com/rasmusbergpalm/DeepLearnToolbox
作者：陆振波
电子
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Code for CVPR15 paper Learning to Compare Image Patches via Convolutional Neural Networks
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This package allows researches to apply the described networks to match image patches and extract corresponding patches.
We tried to make the code
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<\视觉机器学习20讲>配套仿真代码——CNN，卷积神经网络，有注释，适合初学者-Visual simulation code CNN, form a complete set of machine learning about 20 convolutional neural networks, have comments, suitable for beginners
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本文档讨论和实现了卷积神经网络， 并且进行了延伸。非常重要的资料，对于深度学习有很重要的借鉴意义。 -This document discusses the derivation and implementation of convolutional neural networks (CNNs), followed by a few straightforward extensions. Convolutional neural networks involve many more connec
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presented dictionary pair classifierdriven
CNNs for object detection, where dictionary pair
back propagation (DPBP) is proposed for the end-to-end
learning of dictionary pair classifiers and CNN representation,
and sample weighting is adopted
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使用3d卷积神经网络对说话人身份进行识别(Using 3D Convolutional Neural Networks for Speaker Verification)
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人体骨骼的关键点提取，总共十四个点，使用卷积神经网络。(The key points of human skeleton are extracted from a total of fourteen points using convolutional neural networks.)
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