中文版 pytorch
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注意力汇聚:Nadaraya-Watson 核回归
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54 | 20182 | January 28, 2026 |
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汇聚层
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29 | 11230 | January 24, 2026 |
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关于可能的报错
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0 | 390 | January 24, 2026 |
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语言模型和数据集
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39 | 16315 | January 20, 2026 |
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序列模型
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37 | 14618 | January 18, 2026 |
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层和块
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46 | 18011 | January 17, 2026 |
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为什么在 d2l 的 torch 包中没有 train_ch3 这个函数?
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6 | 2898 | December 8, 2025 |
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Softmax回归的简洁实现
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120 | 46379 | December 7, 2025 |
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Softmax回归的从零实现
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155 | 58758 | December 5, 2025 |
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多层感知机
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34 | 22949 | December 3, 2025 |
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目标检测和边界框
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14 | 7780 | December 3, 2025 |
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数值稳定性和模型初始化
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17 | 10050 | December 2, 2025 |
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参数管理
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26 | 16400 | November 30, 2025 |
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风格迁移
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11 | 5358 | November 27, 2025 |
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含并行连结的网络(GoogLeNet)
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28 | 10252 | November 13, 2025 |
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自然语言推断:使用注意力
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3 | 1804 | November 12, 2025 |
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微分
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100 | 47046 | November 10, 2025 |
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实战 Kaggle 比赛:狗的品种识别(ImageNet Dogs)
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15 | 6011 | October 30, 2025 |
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用于预训练BERT的数据集
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19 | 6843 | October 24, 2025 |
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循环神经网络的从零开始实现
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52 | 17408 | October 23, 2025 |
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Dropout
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82 | 31251 | October 16, 2025 |
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图像增广
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20 | 9959 | October 8, 2025 |
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多层感知机的从零实现
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91 | 34673 | September 29, 2025 |
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批量规范化
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48 | 14902 | September 26, 2025 |
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序列到序列学习(seq2seq)
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85 | 25262 | September 22, 2025 |
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语义分割和数据集
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21 | 6976 | September 22, 2025 |
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线性回归的简洁实现
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78 | 46489 | September 21, 2025 |
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情感分析:使用递归神经网络
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12 | 4195 | September 15, 2025 |
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深度卷积神经网络(AlexNet)
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51 | 25337 | September 14, 2025 |
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安装
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132 | 114489 | September 12, 2025 |