中文版 pytorch
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About the pytorch category
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0 | 1503 | January 14, 2021 |
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模型选择、欠拟合和过拟合
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88 | 26402 | March 9, 2026 |
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数据预处理
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192 | 68800 | March 4, 2026 |
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Bahdanau 注意力
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38 | 11153 | March 3, 2026 |
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线性代数
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89 | 46845 | March 3, 2026 |
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图像分类数据集
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73 | 27924 | March 2, 2026 |
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自注意力和位置编码
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26 | 8648 | February 20, 2026 |
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自动求导
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92 | 50857 | February 19, 2026 |
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概率
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76 | 27755 | February 14, 2026 |
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前言
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24 | 34825 | February 10, 2026 |
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多层感知机的简洁实现
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55 | 23130 | February 7, 2026 |
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数据操作
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70 | 65394 | February 6, 2026 |
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线性回归
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92 | 52976 | February 6, 2026 |
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线性回归的从零开始实现
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159 | 70442 | February 2, 2026 |
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注意力汇聚：Nadaraya-Watson 核回归
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54 | 19276 | January 28, 2026 |
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汇聚层
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29 | 10690 | January 24, 2026 |
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关于可能的报错
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0 | 232 | January 24, 2026 |
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语言模型和数据集
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39 | 15465 | January 20, 2026 |
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序列模型
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37 | 13997 | January 18, 2026 |
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层和块
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46 | 17389 | January 17, 2026 |
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锚框
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63 | 19428 | December 13, 2025 |
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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 | 2698 | December 8, 2025 |
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Softmax回归的简洁实现
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120 | 44328 | December 7, 2025 |
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Softmax回归的从零实现
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155 | 56958 | December 5, 2025 |
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多层感知机
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34 | 22149 | December 3, 2025 |
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目标检测和边界框
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14 | 7486 | December 3, 2025 |
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卷积神经网络（LeNet）
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102 | 42167 | December 2, 2025 |
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数值稳定性和模型初始化
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17 | 9813 | December 2, 2025 |
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参数管理
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26 | 15940 | November 30, 2025 |
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风格迁移
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11 | 5154 | November 27, 2025 |