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    <title>Dexiang Zong（宗德祥）</title>
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        <p>备注：点击<a href="#"> 名称 </a>进行跳转。</p>
          <h2>推荐站点</h2>
          <ul>
              <li><a href="http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial">UFLDL：</a><des:code>无监督学习和深度学习 Tutorial，由 Stanford Andrew Ng 团队领导完成，十分适合想要学习深度学习童鞋！</des:code></li>
              <li><a href="https://www.coursera.org" target="_blank">Coursera：</a><des:code> 由 Stanford 教授 Andrew NG 领衔创办的教育平台，它与全世界最顶尖的大学和机构合作，提供任何人可学习的免费在线课程。</des:code></li>
              <li><a href="http://rodrigob.github.io/are_we_there_yet/build/" target="_blank">Classification datasets results：</a><des:code> Discover the current state of the art in objects classification. Include MNIST, CIFAR-10, CIFAR-100, STL-10, SVHN, ILSVRC2012 task1.</des:code></li>
              <li><a href="http://www.wikicfp.com/cfp/" target="_blank">WikiCFP：</a><des:code> WikiCFP is a semantic wiki for Calls For Papers in science and technology fields. There are about 40,000 CFPs on WikiCFP. Over 100,000 researchers use WikiCFP each month</des:code></li>
              <li><a href="http://valse.mmcheng.net/" target="_blank">VALSE在线讨论</a><des:code> 视觉与学习青年学者研讨会(Vision And Learning SEminar, 简称VALSE)的主要目标是为计算机视觉、图像处理、模式识别与机器学习等研究领域内的华人青年学者提供深入学术交流的舞台。VALSE QQ群：364188996，VALSE-B QQ群：422075165</des:code></li>
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          <h2>大牛网站</h2>
          <ul>
              <li><a href="http://ai.stanford.edu/~ang/">Andrew NG（吴恩达）</a><des:code> Andrew Ng is a co-founder of Coursera and the director of the Stanford AI Lab. Ng’s Stanford research group focuses on deep learning, which builds very large neural networks to learn from labeled and unlabeled data.</des:code></li>
              <li><a href="http://www.cs.ubc.ca/~lowe/">David Lowe：</a><des:code> University of British Columbia 教授，计算机视觉界大牛，SIFT 发明人。His research interests include computer vision, object recognition, and computational models of human vision</des:code></li>
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          <h2>开源软件</h2>
          <ul>
              <li><a href="http://www.vlfeat.org" target="_blank">VLFeat：</a><des:code> An open source library implements popular computer vision algorithms including HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift.</des:code></li>
              <li><a href="http://caffe.berkeleyvision.org" target="_blank">Caffe：</a><des:code> Caffe is a deep learning framework developed with cleanliness, readability, and speed in mind. It was created by Yangqing Jia during his PhD at UC Berkeley, and is in active development by the Berkeley Vision and Learning Center (BVLC) and by community contributors.</des:code></li>
              <li><a href="https://code.google.com/p/cuda-convnet" target="_blank">Cuda-Convnet：</a><des:code> This is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm.</des:code></li>
              <li><a href="http://devblogs.nvidia.com/parallelforall/accelerate-machine-learning-cudnn-deep-neural-network-library/" target="_blank">cuDNN：</a><des:code>由 NVIDA 的 Solution Architect 大神L arry开发，看名字就知道是干什么的了，基于CUDA的DNN封装，No Programming Required。cuDNN is integrated into the development branch of the CAFFE neural network toolkit today!  It is expected to be part of the official CAFFE 1.0 release. cuDNN is free for anyone to use for any purpose: academic, research or commercial. </des:code></li>
              <li><a href="http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html" target="_blank">Matlab Toolbox for Dimensionality Reduction（MTDR）：</a><des:code>The Matlab Toolbox for Dimensionality Reduction contains Matlab implementations of 34 techniques for dimensionality reduction and metric learning. Include PCA、Probabilistic PCA、LDA、LLE、LPP、LLE、DAE、and so on. 你懂的！</des:code>
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          <h2>国际会议</h2>
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              <li><a href="http://www.acmmm.org/2014/" target="_blank">ACM Multimedia 2014：</a><des:code> The 22nd ACM International Conference on Multimedia.</des:code></li>
              <li><a href="http://www.cvpapers.com/" target="_blank">CVPapers：</a><des:code> Computer Vision Resource. 收纳了ICCV，CVPR，ECCV，ACCV，BMVC，ICPR，SIGGRAPH，IJCAI等多个会议的有关计算机视觉方面的Paper。</des:code></li>
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