Topic | Replies | Views | Activity | |
---|---|---|---|---|
预训练BERT |
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0 | 721 | November 21, 2022 |
用于预训练BERT的数据集 |
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0 | 696 | November 21, 2022 |
来自Transformers的双向编码器表示(BERT) |
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0 | 769 | November 21, 2022 |
词的相似性和类比任务 |
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0 | 691 | November 21, 2022 |
子词嵌入 |
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0 | 651 | November 21, 2022 |
预训练word2vec |
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0 | 824 | November 21, 2022 |
用于预训练词嵌入的数据集 |
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0 | 754 | November 21, 2022 |
实战Kaggle比赛:狗的品种识别(ImageNet Dogs) |
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0 | 679 | November 21, 2022 |
实战 Kaggle 比赛:图像分类 (CIFAR-10) |
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0 | 751 | November 21, 2022 |
风格迁移 |
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0 | 685 | November 21, 2022 |
门控循环单元(GRU) |
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0 | 784 | November 21, 2022 |
全卷积网络 |
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0 | 764 | November 21, 2022 |
转置卷积 |
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0 | 626 | November 21, 2022 |
语义分割和数据集 |
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0 | 703 | November 21, 2022 |
区域卷积神经网络(R-CNN)系列 |
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0 | 763 | November 21, 2022 |
单发多框检测(SSD) |
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0 | 702 | November 21, 2022 |
目标检测数据集 |
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0 | 768 | November 21, 2022 |
多尺度目标检测 |
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0 | 798 | November 21, 2022 |
锚框 |
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0 | 665 | November 21, 2022 |
目标检测和边界框 |
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0 | 887 | November 21, 2022 |
微调 |
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0 | 645 | November 21, 2022 |
图像增广 |
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0 | 813 | November 21, 2022 |
循环神经网络的简洁实现 |
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0 | 634 | November 21, 2022 |
循环神经网络的从零开始实现 |
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0 | 725 | November 21, 2022 |
循环神经网络 |
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0 | 613 | November 21, 2022 |
语言模型和数据集 |
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0 | 706 | November 21, 2022 |
文本预处理 |
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0 | 665 | November 21, 2022 |
序列模型 |
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0 | 753 | November 21, 2022 |
稠密连接网络(DenseNet) |
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0 | 777 | November 21, 2022 |
残差网络(ResNet) |
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0 | 724 | November 21, 2022 |