摘要:https://blog.csdn.net/qq_14845119/article/details/80787753
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摘要:https://blog.csdn.net/Cyiano/article/details/74928415 https://ziyubiti.github.io/2016/11/06/gradvanish/ https://blog.csdn.net/qq_25737169/article/deta
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摘要:https://aistudio.baidu.com/aistudio/projectdetail/453240 https://www.jiqizhixin.com/articles/122304 http://codewithzhangyi.com/2018/09/09/NLP%E5%AE%9E
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摘要:http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function
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摘要:https://developers.google.com/machine-learning/crash-course/regularization-for-simplicity/video-lecture?hl=zh-cn
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摘要:https://blog.csdn.net/XindiOntheWay/article/details/85220342 https://www.jianshu.com/p/ef3caa5672c8 https://myslide.cn/slides/10614# https://github.co
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摘要:细讲: https://mp.weixin.qq.com/s/RLxWevVWHXgX-UcoxDS70w https://www.jianshu.com/p/b1030350aadb 详解Transformer (Attention Is All You Need) https://zhuanla
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摘要:https://www.zhihu.com/question/23765351
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摘要:https://zhuanlan.zhihu.com/p/58883095 https://zhuanlan.zhihu.com/p/35709485 【学习过程】 交叉熵损失函数经常用于分类问题中,特别是在神经网络做分类问题时,也经常使用交叉熵作为损失函数,此外,由于交叉熵涉及到计算每个类别的概率
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摘要:2019年,异质图神经网络领域有哪些值得读的顶会论文?
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摘要:【Graph Embedding】: metapath2vec算法 https://ericdongyx.github.io/metapath2vec/m2v.html
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