“第十六章 典型网络结构”版本间的差异

来自cslt Wiki
跳转至: 导航搜索
演示链接
 
(相同用户的12个中间修订版本未显示)
第2行: 第2行:
 
*[[教学参考-16|教学参考]]
 
*[[教学参考-16|教学参考]]
 
*[http://aigraph.cslt.org/courses/16/course-16.pptx 课件]
 
*[http://aigraph.cslt.org/courses/16/course-16.pptx 课件]
*小清爱提问:什么是卷积神经网络?[]
+
*小清爱提问:什么是卷积神经网络?[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247489688&idx=1&sn=d574ac8a16eda0dfd094f9d868142534&chksm=c308125af47f9b4cd2e561b538df2572c7d6f744a1cde66965745b9740282eadb6954f24649e&scene=178&cur_album_id=3052762821081645063#rd]
*小清爱提问:什么是循环神经网络?[]
+
*小清爱提问:什么是循环神经网络?[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247489741&idx=1&sn=632d0a9fe6eaa495b0fd729864c7071d&chksm=c308120ff47f9b191992806351f0bab6adf720408443de9be6087607021840e5887af5ef000f&scene=178&cur_album_id=3052762821081645063#rd]
  
 
==扩展阅读==
 
==扩展阅读==
第10行: 第10行:
 
* AI100问:什么是循环神经网络?[http://aigraph.cslt.org/ai100/AI-100-107-什么是循环神经网络.pdf]
 
* AI100问:什么是循环神经网络?[http://aigraph.cslt.org/ai100/AI-100-107-什么是循环神经网络.pdf]
 
* AI100问:什么是自编码器?[http://aigraph.cslt.org/ai100/AI-100-117-什么是自编码器.pdf]
 
* AI100问:什么是自编码器?[http://aigraph.cslt.org/ai100/AI-100-117-什么是自编码器.pdf]
 +
* 维基百科:多层感知器[http://aigraph.cslt.org/courses/16/多层感知器.pdf][http://aigraph.cslt.org/courses/16/Multilayer_perceptron.pdf]
 +
* 维基百科:卷积神经网络[http://aigraph.cslt.org/courses/16/卷积神经网络.pdf][http://aigraph.cslt.org/courses/16/Convolutional_neural_network.pdf]
 +
* 维基百科:循环神经网络[http://aigraph.cslt.org/courses/16/循环神经网络.pdf][http://aigraph.cslt.org/courses/16/Recurrent_neural_network.pdf]
 +
* 维基百科:自编码器[http://aigraph.cslt.org/courses/16/自编码器.pdf][http://aigraph.cslt.org/courses/16/Autoencoder.pdf]
 +
* 机器之心:卷积神经网络 [https://www.jiqizhixin.com/graph/technologies/85c4b79b-6428-4184-b9bc-5beb6e2b1f3f]
 +
* 机器之心:一文简述循环神经网络[https://www.jiqizhixin.com/articles/072203]
 +
* 量子位:什么是自编码器 [https://zhuanlan.zhihu.com/p/34238979]
  
  
 
==视频展示==
 
==视频展示==
* 全连接网络展示 [http://aigraph.cslt.org/courses/FN_visualization.mp4]
+
* 全连接网络展示 [http://aigraph.cslt.org/courses/16/FN_visualization.mp4]
* 全连接层展示 [http://aigraph.cslt.org/courses/Convolution.mp4]
+
* 全连接层展示 [http://aigraph.cslt.org/courses/16/Convolution.mp4]
* Disclaimer CNN 展示 [http://aigraph.cslt.org/courses/CNN_visulization.mp4]
+
* Disclaimer CNN 展示 [http://aigraph.cslt.org/courses/16/CNN_visulization.mp4]
* CNN_Otavio_Good 的CNN展示 [http://aigraph.cslt.org/courses/CNN_Otavio_Good.mp4]
+
* CNN_Otavio_Good 的CNN展示 [http://aigraph.cslt.org/courses/16/CNN_Otavio_Good.mp4]
 +
* 知多少:什么是循环神经网络[http://aigraph.cslt.org/courses/16/知多少_什么是循环神经网络RNN.mp4]
 +
* 知多少:什么是卷积神经网络[http://aigraph.cslt.org/courses/16/知多少_什么是卷积神经网络CNN.mp4]
  
 
==演示链接==
 
==演示链接==
第22行: 第31行:
 
* Neural Net demo [https://phiresky.github.io/neural-network-demo/]
 
* Neural Net demo [https://phiresky.github.io/neural-network-demo/]
 
* Neural Net training demo [http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.30169&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&co]
 
* Neural Net training demo [http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.30169&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&co]
* Quick draw, and let NN guess [https://quickdraw.withgoogle.com/]
+
* Quick draw, and let NN guess [*][https://quickdraw.withgoogle.com/]
  
 
==开发者资源==
 
==开发者资源==
第31行: 第40行:
 
==高级读者==
 
==高级读者==
  
 +
* Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position" [https://www.cs.princeton.edu/courses/archive/spr08/cos598B/Readings/Fukushima1980.pdf]
 +
* Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation Applied to Handwritten Zip Code Recognition; AT&T Bell Laboratories [http://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf]
 +
* Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences. 79 (8): 2554–2558. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC346238]
 +
* Elman, Jeffrey L. (1990). "Finding Structure in Time". Cognitive Science. 14 (2): 179–211. [https://doi.org/10.1016%2F0364-0213%2890%2990002-E]
 +
* Jordan, Michael I. (1997-01-01). "Serial Order: A Parallel Distributed Processing Approach". Neural-Network Models of Cognition - Biobehavioral Foundations. Advances in Psychology. Neural-Network Models of Cognition. Vol. 121. pp. 471–495.  [https://doi.org/10.1016%2Fs0166-4115%2897%2980111-2]
 +
* Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In Advances in neural information processing systems 6 (pp. 3-10). [https://proceedings.neurips.cc/paper/1993/file/9e3cfc48eccf81a0d57663e129aef3cb-Paper.pdf]
 
* 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [http://mlbook.cslt.org]
 
* 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [http://mlbook.cslt.org]
 
* Christopher M. Bishop, Neural Networks for Pattern Recognition [https://www.amazon.com/Networks-Recognition-Advanced-Econometrics-Paperback/dp/0198538642]
 
* Christopher M. Bishop, Neural Networks for Pattern Recognition [https://www.amazon.com/Networks-Recognition-Advanced-Econometrics-Paperback/dp/0198538642]
 
* Christopher M. Bishop, Pattern Recognition and Machine Learning [https://www.amazon.com/-/es/Christopher-M-Bishop/dp/0387310738/ref=d_pd_sbs_sccl_3_3/143-3751675-4420139?pd_rd_w=2LB2s&content-id=amzn1.sym.3676f086-9496-4fd7-8490-77cf7f43f846&pf_rd_p=3676f086-9496-4fd7-8490-77cf7f43f846&pf_rd_r=XM3AJDN6MSM89CR1ZFV7&pd_rd_wg=QjVJC&pd_rd_r=10293f3a-8b44-4f6d-b6ee-9595387e2f18&pd_rd_i=0387310738&psc=1]
 
* Christopher M. Bishop, Pattern Recognition and Machine Learning [https://www.amazon.com/-/es/Christopher-M-Bishop/dp/0387310738/ref=d_pd_sbs_sccl_3_3/143-3751675-4420139?pd_rd_w=2LB2s&content-id=amzn1.sym.3676f086-9496-4fd7-8490-77cf7f43f846&pf_rd_p=3676f086-9496-4fd7-8490-77cf7f43f846&pf_rd_r=XM3AJDN6MSM89CR1ZFV7&pd_rd_wg=QjVJC&pd_rd_r=10293f3a-8b44-4f6d-b6ee-9595387e2f18&pd_rd_i=0387310738&psc=1]

2023年8月13日 (日) 01:41的最后版本

教学资料

  • 教学参考
  • 课件
  • 小清爱提问:什么是卷积神经网络?[1]
  • 小清爱提问:什么是循环神经网络?[2]

扩展阅读

  • AI100问:什么是卷积神经网络?[3]
  • AI100问:什么是循环神经网络?[4]
  • AI100问:什么是自编码器?[5]
  • 维基百科:多层感知器[6][7]
  • 维基百科:卷积神经网络[8][9]
  • 维基百科:循环神经网络[10][11]
  • 维基百科:自编码器[12][13]
  • 机器之心:卷积神经网络 [14]
  • 机器之心:一文简述循环神经网络[15]
  • 量子位:什么是自编码器 [16]


视频展示

  • 全连接网络展示 [17]
  • 全连接层展示 [18]
  • Disclaimer CNN 展示 [19]
  • CNN_Otavio_Good 的CNN展示 [20]
  • 知多少:什么是循环神经网络[21]
  • 知多少:什么是卷积神经网络[22]

演示链接

  • Andrej Karpathy's CNN demo [23]
  • Neural Net demo [24]
  • Neural Net training demo [25]
  • Quick draw, and let NN guess [*][26]

开发者资源

  • Python package for neural nets: PyTorch [27] TensorFlow[28] NeuralLab[29]


高级读者

  • Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position" [30]
  • Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Backpropagation Applied to Handwritten Zip Code Recognition; AT&T Bell Laboratories [31]
  • Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences. 79 (8): 2554–2558. [32]
  • Elman, Jeffrey L. (1990). "Finding Structure in Time". Cognitive Science. 14 (2): 179–211. [33]
  • Jordan, Michael I. (1997-01-01). "Serial Order: A Parallel Distributed Processing Approach". Neural-Network Models of Cognition - Biobehavioral Foundations. Advances in Psychology. Neural-Network Models of Cognition. Vol. 121. pp. 471–495. [34]
  • Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In Advances in neural information processing systems 6 (pp. 3-10). [35]
  • 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [36]
  • Christopher M. Bishop, Neural Networks for Pattern Recognition [37]
  • Christopher M. Bishop, Pattern Recognition and Machine Learning [38]