“第十八章 深度学习前沿”版本间的差异

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*[[教学参考-18|教学参考]]
 
*[[教学参考-18|教学参考]]
 
*[http://aigraph.cslt.org/courses/18/course-18.pptx 课件]
 
*[http://aigraph.cslt.org/courses/18/course-18.pptx 课件]
 +
*小清爱提问：什么是词向量[http://aigraph.cslt.org/ai100/AI100-119-什么是词向量.pdf]
 +
*小清爱提问：什么是序列到序列模型[http://aigraph.cslt.org/ai100/AI100-120-什么是序列到序列模型.pdf]
 +
*小清爱提问：什么是注意力机制[http://aigraph.cslt.org/ai100/AI100-121-什么是注意力机制.pdf]
 +
*小清爱提问：什么是自监督学习[http://aigraph.cslt.org/ai100/AI100-122-什么是自监督学习.pdf]
 +
*小清爱提问：什么是对抗生成网络[http://aigraph.cslt.org/ai100/AI100-123-什么是对抗生成网络.pdf]
 +
*小清爱提问：什么是变分自编码器[http://aigraph.cslt.org/ai100/AI100-124-什么是变分自编码器.pdf]
  
 
==扩展阅读==
 
==扩展阅读==
  
−
 
+
*AI100问：什么是残差网络[http://aigraph.cslt.org/ai100/AI-100-118-什么是残差网络.pdf]
−
 
+
*AI100问：什么是词向量[http://aigraph.cslt.org/ai100/AI100-119-什么是词向量.pdf]
 +
*AI100问：什么是序列到序列模型[http://aigraph.cslt.org/ai100/AI100-120-什么是序列到序列模型.pdf]
 +
*AI100问：什么是注意力机制[http://aigraph.cslt.org/ai100/AI100-121-什么是注意力机制.pdf]
 +
*AI100问：什么是自监督学习[http://aigraph.cslt.org/ai100/AI100-122-什么是自监督学习.pdf]
 +
*AI100问：什么是对抗生成网络[http://aigraph.cslt.org/ai100/AI100-123-什么是对抗生成网络.pdf]
 +
*AI100问：什么是变分自编码器[http://aigraph.cslt.org/ai100/AI100-124-什么是变分自编码器.pdf]
  
  
 
==视频展示==
 
==视频展示==
  
 +
* char embedding [http://aigraph.cslt.org/courses/18/mnist-embedding.mp4]
 +
* word embedding [http://aigraph.cslt.org/courses/18/word-embedding.mp4]
 +
* Picture embedding [http://aigraph.cslt.org/courses/18/picture-embedding.mp4]
 +
* GAN training [http://aigraph.cslt.org/courses/18/GAN.mp4]
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* VAE latent space [http://aigraph.cslt.org/courses/18/VAE-space.mp4]
 +
* VAE training process [http://aigraph.cslt.org/courses/18/VAE-training.mp4]
  
 
==演示链接==
 
==演示链接==
  
−
* HoggingFace 演示[https://www.kdnuggets.com/2022/05/top-10-machine-learning-demos-hugging-face-spaces-edition.html]
+
* Word embedding online training [https://remykarem.github.io/word2vec-demo/]
−
* Quick style transfer [https://tenso.rs/demos/fast-neural-style/]
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* Word Embedding [https://www.cs.cmu.edu/~dst/WordEmbeddingDemo/]
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* Pix2Pix[https://affinelayer.com/pixsrv/]
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* WebVectors: Word Embedding Online [http://vectors.nlpl.eu/explore/embeddings/en/]
−
* AutoWriter[https://cyborg.tenso.rs/]
+
* 那些不存在的人 [https://thispersondoesnotexist.com/]
 +
* GAN painting，在图上添加或移除内容 [http://gandissect.res.ibm.com/ganpaint.html?project=churchoutdoor&layer=layer4]
  
 
==开发者资源==
 
==开发者资源==
  
 +
* Word embedding [https://rpubs.com/alipphardt/478255][https://github.com/tmikolov/word2vec]
 +
* Facebook sequence to sequence model [https://github.com/facebookresearch/fairseq]
 +
* Keras sequence to sequence mdoel, very simple [https://github.com/farizrahman4u/seq2seq]
 +
* A simple GAN implementation [https://iq.opengenus.org/beginners-guide-to-generative-adversarial-networks/]
 +
* Pytroch-GAN: A large amount of GAN implementation [https://github.com/eriklindernoren/PyTorch-GAN]
 +
* A collectio nof generative models (GAN/VAE/RBM) [https://github.com/wiseodd/generative-models]
  
  

2022年8月8日 (一) 01:53的最后版本


教学资料

  • 教学参考
  • 课件
  • 小清爱提问：什么是词向量[1]
  • 小清爱提问：什么是序列到序列模型[2]
  • 小清爱提问：什么是注意力机制[3]
  • 小清爱提问：什么是自监督学习[4]
  • 小清爱提问：什么是对抗生成网络[5]
  • 小清爱提问：什么是变分自编码器[6]

扩展阅读

  • AI100问：什么是残差网络[7]
  • AI100问：什么是词向量[8]
  • AI100问：什么是序列到序列模型[9]
  • AI100问：什么是注意力机制[10]
  • AI100问：什么是自监督学习[11]
  • AI100问：什么是对抗生成网络[12]
  • AI100问：什么是变分自编码器[13]


视频展示

  • char embedding [14]
  • word embedding [15]
  • Picture embedding [16]
  • GAN training [17]
  • VAE latent space [18]
  • VAE training process [19]

演示链接

  • Word embedding online training [20]
  • Word Embedding [21]
  • WebVectors: Word Embedding Online [22]
  • 那些不存在的人 [23]
  • GAN painting，在图上添加或移除内容 [24]

开发者资源

  • Word embedding [25][26]
  • Facebook sequence to sequence model [27]
  • Keras sequence to sequence mdoel, very simple [28]
  • A simple GAN implementation [29]
  • Pytroch-GAN: A large amount of GAN implementation [30]
  • A collectio nof generative models (GAN/VAE/RBM) [31]


高级读者

  • He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.[32]
  • Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model[J]. Advances in neural information processing systems, 2000, 13. [33]
  • Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013. [34]
  • Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823. [35]
  • Li C, Ma X, Jiang B, et al. Deep speaker: an end-to-end neural speaker embedding system[J]. arXiv preprint arXiv:1705.02304, 2017. [36]
  • Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Twenty-ninth AAAI conference on artificial intelligence. 2015. [37]
  • Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks[J]. Advances in neural information processing systems, 2014, 27. [38]
  • Liu Y, Liu D, Lv J, et al. Generating Chinese poetry from images via concrete and abstract information[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. [39]
  • Sun D, Ren T, Li C, et al. Learning to write stylized chinese characters by reading a handful of examples[J]. arXiv preprint arXiv:1712.06424, 2017. [40]
  • Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014. [41]
  • Xu K, Ba J, Kiros R, et al. Show, attend and tell: Neural image caption generation with visual attention[C]//International conference on machine learning. PMLR, 2015: 2048-2057. [42]
  • Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30. [43]
  • Liu X, Zhang F, Hou Z, et al. Self-supervised learning: Generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021. [44]
  • Schneider S, Baevski A, Collobert R, et al. wav2vec: Unsupervised pre-training for speech recognition[J]. arXiv preprint arXiv:1904.05862, 2019. [45]
  • Noroozi M, Favaro P. Unsupervised learning of visual representations by solving jigsaw puzzles[C]//European conference on computer vision. Springer, Cham, 2016: 69-84. [46]
  • Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. [47]
  • Brown T, Mann B, Ryder N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33: 1877-1901. [48]
  • Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8): 9.[]
  • Ethayarajh K. How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings[J]. arXiv preprint arXiv:1909.00512, 2019. [49]
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[J]. Advances in neural information processing systems, 2014, 27. [50]
  • Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. [51]
  • 王东，机器学习导论,第三章，神经模型，2021,清华大学出版社 [52]
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning [53]
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