“第十七章 深度学习”版本间的差异

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高级读者
 
(相同用户的9个中间修订版本未显示)
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==扩展阅读==
 
==扩展阅读==
  
 
+
*AI100问:什么是深度学习?[http://aigraph.cslt.org/ai100/AI-100-48-什么是深度学习.pdf]
 +
*维基百科:深度学习 [http://aigraph.cslt.org/courses/17/深度学习.pdf][http://aigraph.cslt.org/courses/17/Deep_learning.pdf]
 +
*维基百科:杰弗里·辛顿 [http://aigraph.cslt.org/courses/17/杰弗里·辛顿.pdf][http://aigraph.cslt.org/courses/17/Geoffrey_Hinton.pdf]
 +
*维基百科:约书亚·本希奥 [http://aigraph.cslt.org/courses/17/约书亚·本希奥.pdf][http://aigraph.cslt.org/courses/17/Yoshua_Bengio.pdf]
 +
*维基百科:杨立昆 [http://aigraph.cslt.org/courses/17/杨立昆.pdf][http://aigraph.cslt.org/courses/17/Yann_LeCun.pdf]
 +
*维基百科:通用近似定理[http://aigraph.cslt.org/courses/17/Universal_approximation_theorem.pdf][http://aigraph.cslt.org/courses/17/通用近似定理.pdf]
  
 
==视频展示==
 
==视频展示==
  
 
*VGG Net visualization [http://aigraph.cslt.org/courses/17/VGG16.mp4]
 
*VGG Net visualization [http://aigraph.cslt.org/courses/17/VGG16.mp4]
 +
*Disclaimer CNN 展示 [http://aigraph.cslt.org/courses/16/CNN_visulization.mp4]
  
 
==演示链接==
 
==演示链接==
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* Pix2Pix[https://affinelayer.com/pixsrv/]
 
* Pix2Pix[https://affinelayer.com/pixsrv/]
 
* AutoWriter[https://cyborg.tenso.rs/]
 
* AutoWriter[https://cyborg.tenso.rs/]
 +
* HoggingFace 演示[https://www.kdnuggets.com/2022/05/top-10-machine-learning-demos-hugging-face-spaces-edition.html]
 +
* CNN visualization [https://distill.pub/2017/feature-visualization/]
  
 
==开发者资源==
 
==开发者资源==
  
 
*Georgia Tech, Polo Club (可解释机器学习) [https://poloclub.github.io/]
 
*Georgia Tech, Polo Club (可解释机器学习) [https://poloclub.github.io/]
*Google developer courses [https://developers.google.com/machine-learning/crash-course?hl=zh-cn]
+
*Google developer courses [*][https://developers.google.com/machine-learning/crash-course?hl=zh-cn]
* ConvNetJS 代码 [https://github.com/karpathy/convnetjs]
+
*ConvNetJS 代码 [https://github.com/karpathy/convnetjs]
  
 
==高级读者==
 
==高级读者==
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* Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. science, 2006, 313(5786): 504-507. [https://asset-pdf.scinapse.io/prod/2100495367/2100495367.pdf]
 
* Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. science, 2006, 313(5786): 504-507. [https://asset-pdf.scinapse.io/prod/2100495367/2100495367.pdf]
 
* Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1527-1554. [https://www.cs.utoronto.ca/~hinton/absps/ncfast.pdf]
 
* Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1527-1554. [https://www.cs.utoronto.ca/~hinton/absps/ncfast.pdf]
 
+
* Universal approximation theorem [*][https://medium.com/analytics-vidhya/neural-networks-and-the-universal-approximation-theorem-e5c387982eed]
 
* 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]
 
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [https://www.deeplearningbook.org/]
 
* Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [https://www.deeplearningbook.org/]

2023年8月13日 (日) 01:43的最后版本

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

  • AI100问:什么是深度学习?[3]
  • 维基百科:深度学习 [4][5]
  • 维基百科:杰弗里·辛顿 [6][7]
  • 维基百科:约书亚·本希奥 [8][9]
  • 维基百科:杨立昆 [10][11]
  • 维基百科:通用近似定理[12][13]

视频展示

  • VGG Net visualization [14]
  • Disclaimer CNN 展示 [15]

演示链接

  • ConvNetJS 深度神经网络演示 [16]
  • Leiden Demo for image classification [17]
  • CNN explainer[18]
  • Quick style transfer [19]
  • Pix2Pix[20]
  • AutoWriter[21]
  • HoggingFace 演示[22]
  • CNN visualization [23]

开发者资源

  • Georgia Tech, Polo Club (可解释机器学习) [24]
  • Google developer courses [*][25]
  • ConvNetJS 代码 [26]

高级读者

  • LeCun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436-444.[27]
  • Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25. [28]
  • Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. science, 2006, 313(5786): 504-507. [29]
  • Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1527-1554. [30]
  • Universal approximation theorem [*][31]
  • 王东,机器学习导论,第三章,神经模型,2021,清华大学出版社 [32]
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [33]