中文版 pytorch
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|---|---|---|---|---|
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权重衰减
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59 | 21858 | March 28, 2026 |
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模型选择、欠拟合和过拟合
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89 | 28872 | March 27, 2026 |
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多头注意力
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61 | 25613 | March 16, 2026 |
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数据预处理
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192 | 73485 | March 4, 2026 |
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Bahdanau 注意力
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38 | 12077 | March 3, 2026 |
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图像分类数据集
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73 | 29927 | March 2, 2026 |
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自动求导
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92 | 53611 | February 19, 2026 |
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概率
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76 | 29659 | February 14, 2026 |
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多层感知机的简洁实现
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55 | 24613 | February 7, 2026 |
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数据操作
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70 | 69328 | February 6, 2026 |
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线性回归的从零开始实现
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159 | 74929 | February 2, 2026 |
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注意力汇聚：Nadaraya-Watson 核回归
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54 | 20873 | January 28, 2026 |
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汇聚层
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29 | 11554 | January 24, 2026 |
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关于可能的报错
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0 | 516 | January 24, 2026 |
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语言模型和数据集
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39 | 17044 | January 20, 2026 |
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序列模型
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37 | 15075 | January 18, 2026 |
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层和块
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46 | 18511 | 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 | 3038 | December 8, 2025 |
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多层感知机
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34 | 23757 | December 3, 2025 |
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目标检测和边界框
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14 | 8097 | December 3, 2025 |
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数值稳定性和模型初始化
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17 | 10368 | December 2, 2025 |
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参数管理
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26 | 16885 | November 30, 2025 |
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风格迁移
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11 | 5535 | November 27, 2025 |
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含并行连结的网络（GoogLeNet）
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28 | 10554 | November 13, 2025 |
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自然语言推断：使用注意力
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3 | 1946 | November 12, 2025 |
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微分
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100 | 48152 | 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 | 6196 | 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 | 18152 | October 23, 2025 |
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图像增广
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20 | 10293 | October 8, 2025 |
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批量规范化
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48 | 15392 | September 26, 2025 |