“第十七章 深度学习”版本间的差异
来自cslt Wiki
（→高级读者） |
|||
| （相同用户的2个中间修订版本未显示） | |||
| 第29行： | 第29行： | ||
* 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] | * 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] | ||
| 第42行： | 第43行： | ||
* 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] | + | * 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
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
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
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]
