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预备知识：图像处理的基本方法， RBM 的实现，多重神经网络实现， C++ 编程 算法：卷积神经网络，多层 RBM 及 Sparse Coding 的变种， ICA 的多层网络

预备知识：图像处理的基本方法， RBM 的实现，多重神经网络实现， C++ 编程 算法：卷积神经网络，多层 RBM 及 Sparse Coding 的变种， ICA 的多层网络 工具： GPU 编程， MPI 多机通信控制， BLAS 相关的 routine 硬件：构建 Infiniband GPU 集群 实现语言： C++ ， python( 处理脚本 ). 算法相关论文.

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预备知识：图像处理的基本方法， RBM 的实现，多重神经网络实现， C++ 编程 算法：卷积神经网络，多层 RBM 及 Sparse Coding 的变种， ICA 的多层网络

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  1. 预备知识：图像处理的基本方法，RBM的实现，多重神经网络实现，C++编程预备知识：图像处理的基本方法，RBM的实现，多重神经网络实现，C++编程 • 算法：卷积神经网络，多层RBM及Sparse Coding的变种，ICA的多层网络 • 工具：GPU编程，MPI多机通信控制，BLAS相关的routine • 硬件：构建Infiniband GPU集群 • 实现语言：C++，python(处理脚本)

  2. 算法相关论文 • LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.(卷积神经网络起源) • Lee H, Grosse R, Ranganath R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009: 609-616.(卷积玻尔兹曼机，人脸特征提取) • Krizhevsky A. Convolutional deep belief networks on cifar-10[J]. Unpublished manuscript, 2010.(卷积玻尔兹曼机的实现细节) • Le Q V, Karpenko A, Ngiam J, et al. ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning[C]//NIPS. 2011: 1017-1025.(Google大规模网络中实现的算法基础，上述算法的变种)

  3. GPU及大规模计算论文 • Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[C]//NIPS. 2012, 1(2): 4.(ImageNet大规模数据的尝试及单机多GPU模式) • Le Q V.Building high-level features using large scale unsupervised learning[C]//Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013: 8595-8598.(1000台机器集群,Google的尝试) • Coates A, Huval B, Wang T, et al. Deep learning with COTS HPC systems[C]//Proceedings of The 30th International Conference on Machine Learning. 2013: 1337-1345.(3台GPU Infiniband集群，实现楼上的结果，我们的目标)

  4. 其他工具和软件 • MVAPICH2：MPI软件，集群通信控制 • GPU编程：Programming massively parallel processors (2nd edition) • BLAS：矩阵运算 数据源 • CIFAR-10 • ImageNet • Kyoto http://dippix.tp.chiba-u.jp/database/index_e.html • Caltech-101

  5. 基本规划 (1)实现Lee文章中卷积RBM无监督对图片特征提取(2-3 weeks) (2)实现Hinton对cifar-10或ImageNet对图片分类预测，单机多GPU(3-4 weeks) (3)实现Coates用Infiniband GPU集群对大规模图片的无监督训练(1-2 months)

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