“第十七章 深度学习”版本间的差异

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==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]
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* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [https://www.deeplearningbook.org/]
* 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]
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2022年8月5日 (五) 10:30的版本

教学资料

  • 教学参考
  • 课件
  • 小清爱提问:什么是深度学习(上)?[1]
  • 小清爱提问:什么是深度学习(下)?[2]

扩展阅读

视频展示

演示链接

  • ConvNetJS 深度神经网络演示 [3]
  • Leiden Demo for image classification [4]
  • CNN explainer[5]
  • Quick style transfer [6]
  • Pix2Pix[7]
  • AutoWriter[8]

开发者资源

  • Georgia Tech, Polo Club (可解释机器学习) [9]
  • Google developer courses [10]
  • ConvNetJS 代码 [11]

高级读者

  • 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [12]
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [13]