中文版 pytorch
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图像分类数据集
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73 | 29370 | March 2, 2026 |
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自动求导
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92 | 52900 | February 19, 2026 |
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概率
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76 | 29182 | February 14, 2026 |
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多层感知机的简洁实现
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55 | 24240 | February 7, 2026 |
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数据操作
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70 | 68492 | February 6, 2026 |
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线性回归
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92 | 55881 | February 6, 2026 |
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线性回归的从零开始实现
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159 | 73849 | February 2, 2026 |
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注意力汇聚：Nadaraya-Watson 核回归
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54 | 20484 | January 28, 2026 |
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汇聚层
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29 | 11385 | January 24, 2026 |
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关于可能的报错
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0 | 435 | January 24, 2026 |
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语言模型和数据集
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39 | 16656 | January 20, 2026 |
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序列模型
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37 | 14830 | January 18, 2026 |
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层和块
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46 | 18229 | 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 | 2957 | December 8, 2025 |
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Softmax回归的简洁实现
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120 | 46924 | December 7, 2025 |
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Softmax回归的从零实现
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155 | 59434 | December 5, 2025 |
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多层感知机
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34 | 23366 | December 3, 2025 |
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目标检测和边界框
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14 | 7935 | December 3, 2025 |
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数值稳定性和模型初始化
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17 | 10169 | December 2, 2025 |
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参数管理
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26 | 16576 | November 30, 2025 |
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风格迁移
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11 | 5430 | November 27, 2025 |
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含并行连结的网络（GoogLeNet）
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28 | 10370 | November 13, 2025 |
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自然语言推断：使用注意力
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3 | 1865 | November 12, 2025 |
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微分
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100 | 47529 | 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 | 6096 | October 30, 2025 |
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循环神经网络的从零开始实现
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52 | 17726 | October 23, 2025 |
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Dropout
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82 | 31628 | October 16, 2025 |
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图像增广
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20 | 10115 | October 8, 2025 |
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多层感知机的从零实现
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91 | 35065 | September 29, 2025 |
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批量规范化
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48 | 15103 | September 26, 2025 |