├── .github └── FUNDING.yml ├── .gitignore ├── 1-1,结构化数据建模流程范例.ipynb ├── 1-2,图片数据建模流程范例.ipynb ├── 1-3,文本数据建模流程范例.ipynb ├── 1-4,时间序列数据建模流程范例.ipynb ├── 2-1,张量数据结构.ipynb ├── 2-2,自动微分机制.ipynb ├── 2-3,动态计算图.ipynb ├── 3-1,低阶API示范.ipynb ├── 3-2,中阶API示范.ipynb ├── 3-3,高阶API示范.ipynb ├── 4-1,张量的结构操作.ipynb ├── 4-2,张量的数学运算.ipynb ├── 4-3,nn.functional和nn.Module.ipynb ├── 5-1,Dataset和DataLoader.ipynb ├── 5-2,模型层.ipynb ├── 5-3,损失函数.ipynb ├── 5-4,TensorBoard可视化.ipynb ├── 6-1,构建模型的3种方法.ipynb ├── 6-2,训练模型的3种方法.ipynb ├── 6-3,使用GPU训练模型.ipynb ├── 7-1,推荐算法业务.ipynb ├── 7-2,广告算法业务.ipynb ├── 7-3,FM模型.ipynb ├── 7-4,DeepFM模型.ipynb ├── 7-5,FiBiNET模型.ipynb ├── 7-6,DeepCross模型.ipynb ├── 7-7,DIN网络.ipynb ├── 7-8,DIEN网络.ipynb ├── A-1,Kaggle免费GPU使用攻略.ipynb ├── A-2,Streamlit构建机器学习应用.ipynb ├── A-3,使用MacM1芯片加速pytorch.ipynb ├── A-4,optuna可视化调参魔法指南.ipynb ├── A-5,Gradio让你的机器学习模型性感起来.ipynb ├── A-6,30分钟吃掉wandb可视化模型分析.ipynb ├── A-7,30分钟吃掉wandb可视化自动调参.ipynb ├── LICENSE ├── README.md ├── data ├── 1-1-AUC曲线.jpg ├── 1-1-Label分布.jpg ├── 1-1-Loss曲线.jpg ├── 1-1-accuracy.png ├── 1-1-dfhistory.png ├── 1-1-loss.png ├── 1-1-年龄分布.jpg ├── 1-1-年龄相关性.jpg ├── 1-1-数据集展示.jpg ├── 1-1-模型结构.jpg ├── 1-1-训练进度.jpg ├── 1-1-预测概率.jpg ├── 1-1-预测类别.jpg ├── 1-2-Accuracy曲线.jpg ├── 1-2-Loss曲线.jpg ├── 1-2-auc曲线.png ├── 1-2-dfhistory.jpg ├── 1-2-dfhistory.png ├── 1-2-loss曲线.png ├── 1-2-tensorboard.jpg ├── 1-2-图片预览.jpg ├── 1-2-查看样本.png ├── 1-2-模型结构.jpg ├── 1-2-训练过程.jpg ├── 1-3-accuracy曲线.png ├── 1-3-loss曲线.png ├── 1-3-模型结构.jpg ├── 1-3-训练进度.jpg ├── 1-4-dfdiff.png ├── 1-4-torch损失曲线.png ├── 1-4-torch预测.png ├── 1-4-torch预测死亡.png ├── 1-4-torch预测治愈.png ├── 1-4-torch预测确诊.png ├── 1-4-新增人数.png ├── 1-4-新增曲线.png ├── 1-4-日期3月10.png ├── 1-4-累积曲线.png ├── 2-3-计算图可视化.png ├── 3-1-分类数据可视化.png ├── 3-1-分类结果可视化.png ├── 3-1-回归数据可视化.png ├── 3-1-回归结果可视化.png ├── 3-2-01-回归数据可视化.png ├── 3-2-02-回归结果可视化.png ├── 3-2-03-分类数据可视化.png ├── 3-2-04-分类结果可视化.png ├── 3-2-分类数据可视化.png ├── 3-2-分类结果可视化.png ├── 3-2-回归结果可视化.png ├── 3-2-线性回归数据可视化.png ├── 3-2-输出01.jpg ├── 3-3-01-回归数据可视化.png ├── 3-3-02-回归结果可视化.png ├── 3-3-03-分类数据可视化.png ├── 3-3-04-分类结果可视化.png ├── 3-3-dfhistory.png ├── 3-3-loss曲线.png ├── 3-3-mape曲线.png ├── 3-3-分类acc曲线.png ├── 3-3-分类loss曲线.png ├── 3-3-分类数据可视化.png ├── 3-3-分类结果可视化.png ├── 3-3-回归数据可视化.png ├── 3-3-回归结果可视化.png ├── 5-1-car2.jpg ├── 5-1-car9.jpg ├── 5-1-cifar2预览.jpg ├── 5-1-傻乎乎.png ├── 5-1-旋转.png ├── 5-1-翻转.png ├── 5-1-词典.png ├── 5-2-01-模型评估.jpg ├── 5-2-Transformer结构.jpg ├── 5-2-归一化技术对比.png ├── 5-3-focal_loss预测结果.png ├── 5-3-同心圆分布.png ├── 5-4-graph结构.png ├── 5-4-九宫格.png ├── 5-4-原始图像可视化.png ├── 5-4-可视化人工绘图.png ├── 5-4-张量分布.png ├── 5-4-指标变化.png ├── 6-2-minist.png ├── Python数据分析.png ├── Python编程ABC.png ├── Python网络爬虫入门.png ├── Python网络爬虫进阶.png ├── Pytorch官方vs吃掉Pytorch.png ├── cat.jpeg ├── cifar2.jpg ├── stacking融合.jpeg ├── torch动态图.gif ├── 电影评论.jpg ├── 疫情前后对比.png ├── 算法美食屋二维码.jpg └── 算法美食屋二维码.png ├── push-to-github.ipynb ├── 一、Pytorch的建模流程.ipynb ├── 七、Pytorch与广告推荐.ipynb ├── 三、Pytorch的层次结构.ipynb ├── 二、Pytorch的核心概念.ipynb ├── 五、Pytorch的中阶API.ipynb ├── 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