“第十六章 典型网络结构”版本间的差异
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*[[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®Dataset=reg-plane&learningRate=0.03®ularizationRate=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®Dataset=reg-plane&learningRate=0.03®ularizationRate=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
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
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]
