├── LICENSE └── docs ├── .nojekyll ├── 02-Markdown快速入门.md ├── 03-Git快速入门.md ├── 04-数据操作及预处理.md ├── 05-线性代数.md ├── 06-矩阵计算.md ├── 07-自动求导.md ├── 08-线性回归.md ├── 09-Softmax回归.md ├── 10-多层感知机.md ├── 11-模型选择+过拟合和欠拟合.md ├── 12-权重衰退.md ├── 13-丢弃法.md ├── 14-数值稳定性&模型初始化&激活函数.md ├── 15-Kaggle加州房价预测案例.md ├── 16-Pytorch神经网络基础.md ├── 17-卷积神经网络.md ├── 18-填充和步幅.md ├── 19-卷积层通道.md ├── 20-池化层.md ├── 21-LeNet.md ├── 22-AlexNet.md ├── 23-VGG.md ├── 24-NiN.md ├── 25-含并行连结的网络.md ├── 26-批量归一化.md ├── 27-残差网络ResNet.md ├── 28-深度学习硬件和相关技术.md ├── 29-数据增广.md ├── 30-微调.md ├── 31-Kaggle_CIFAR10.md ├── 32-物体检测和数据集.md ├── 33-锚框.md ├── 34-物体检测算法.md ├── 35-SSD的实现.md ├── 36-语义分割.md ├── 37-转置卷积.md ├── 38-全连接神经网络.md ├── 39-样式迁移.md ├── 40-序列模型.md ├── 41-文本预处理.md ├── 42-语言模型.md ├── 43-循环神经网络RNN.md ├── 44-门控循环单元(GRU).md ├── 45-深层循环循环网络.md ├── 46-双向循环神经网络.md ├── 47-长短期记忆网络(LSTM).md ├── 48-机器翻译数据集.md ├── 49-编码器和解码器.md ├── 50-序列到序列学习(seq2seq).md ├── 51-束搜索.md ├── 52-注意力机制.md ├── 53-注意力分数.md ├── 54-使用注意力机制的seq2seq.md ├── 55-自注意力.md ├── 56-Transformer.md ├── 57-BERT.md ├── 58-优化算法.md ├── 59-课程总结与进阶学习.md ├── Images ├── 037-01.png ├── 037-02.jpg ├── 038-02.png ├── 038-03.png ├── 039-01.gif ├── 039-02.png ├── 040-01.png ├── 040-02.png ├── 040-03.png ├── 040-04.png ├── 040-05.png ├── 041-01.png ├── 041-02.png ├── 041-03.png ├── 043-01.gif ├── 043-02.png ├── 043-03.png ├── 043-04.png ├── 043-05.png ├── 043-06.gif ├── 043-07.gif ├── 043-addition.gif ├── 044-01.png ├── 044-02.png ├── 044-03.png ├── 044-04.gif ├── 045-01.png ├── 045-02.png ├── 046-01.png ├── 047-01.png ├── 047-02.gif ├── 047-03.png ├── 047-04.png ├── 048-01.png ├── 049-01.gif ├── 049-02.png ├── 049-02（2）.jpg ├── 050-01.png ├── 050-02.png ├── 050-02.ppm ├── 050-03.jpg ├── 050-03.png ├── 051-01.gif ├── 052-01.png ├── 052-02.png ├── 052-03.png ├── 052-04.png ├── 052-05.png ├── 052-06.png ├── 053-01.png ├── 053-02.png ├── 054-01.gif ├── 054-02.png ├── 054-04.png ├── 054.03.png ├── 055-01.png ├── 055-02.png ├── 055-03.gif ├── 055-04.png ├── 056-01.png ├── 056-02.png ├── 056-03.png ├── 056-04.png ├── 056-05.png ├── 057-01.gif ├── 057-02.png ├── 057-03.jpg ├── 057-04.png ├── 058-02.png ├── 058-03.png ├── 058-04.jpg ├── 058-06.png ├── 058-07.png ├── 058-09.gif ├── 0rs9l.gif ├── 1_-eLjPY7UGSoQhSyW5qC6gw.gif ├── 1_3B8iO-se13vA2QfZ4OBRSw.png ├── 1_4LnmIbcIiHM0g8q_XJrD7A.gif ├── 1_4gGddZpKeNIPBoVxYECd5w.png ├── 1_56UGqknHwtMc9RHRRobL8Q.png ├── 1_9GTEzcO8KxxrfutmtsPs3Q.png ├── 1_Hz6t-tokG1niaUfmcysusw.jpeg ├── 1_L9SPSTIq_ptT6a5ejgzmAQ.png ├── 1_O9ulXVmjBustHGLi1zRthA.png ├── 1_REPHY47zAyzgbNKC6zlvBQ.png ├── 1_WfKerFhMvUGti7MWVQ81XQ.png ├── 1_bIY_8FE7dJaGk1kjU7iVSg.png ├── 1_fpmtYoP9e8hycFIILPfW5A.png ├── 1_ulfFYH5HbWpLTIfuebj5mQ.gif ├── 3-Figure1-1.png ├── 38-01.png ├── AI_develop1.jpg ├── AI_develop2.jpg ├── AMD_Nvidia.webp ├── AndrewNG_dropout.PNG ├── Batch_Normalization.gif ├── Comparison_convolution_correlation.svg ├── ConvImgEdge.png ├── DifferentKernel.png ├── L2_regular.png ├── LeNet.gif ├── P4HT.jpg ├── Results-normalization-imagenet-resnet50.png ├── SSD.jpg ├── Screenshot-2019-07-21-at-3.05.56-AM.png ├── Screenshot-from-2021-03-18-12-52-17.png ├── Test.jpg ├── The-lower-level-features-progressively-combine-to-form-higher-layer-features-in-deep_Q640.jpg ├── Weierstrass.jpg ├── WeierstrassFunction.svg ├── Weierstrass_Animation.gif ├── XOR.png ├── aHViPTcyNTE0JmNtZD1pdGVtZWRpdG9yaW1hZ2UmZmlsZW5hbWU9aXRlbWVkaXRvcmltYWdlXzVlODRmZTBhZGVlMGMuanBnJnZlcnNpb249MDAwMCZzaWc9ZmZhNjY3ZjgzOGU5ZTg2ODVjNzVjY2I0MDhkNWJkOGI.jpeg ├── adam_vs_sgd_momeunt.gif ├── andrew_ng_weight_init.PNG ├── apple-m1.jpg ├── arc.png ├── batch_norm.jpg ├── batch_norm2.jpg ├── batch_norm3.jpg ├── beauty_of_math.jpg ├── clamp.png ├── cnn_rgb.gif ├── computation_graph_andrew.PNG ├── conv-full-layer.gif ├── convolution.gif ├── cov_1x1.jpg ├── create_issue.png ├── d2l.png ├── data_normalization.png ├── dropout_or_not.png ├── fpga.jpg ├── git_tutorials_1.png ├── git_tutorials_2.png ├── git_tutorials_3.png ├── graph-normalization-methods.png ├── gray-user-journey.png ├── hardware.png ├── imagenet.jpg ├── images.png ├── inception-full.svg ├── inception.jpg ├── increasing-momentum.gif ├── information_bottleneck.jpg ├── kaggle_California.png ├── kaggle_data.jpg ├── latex_1.webp ├── latex_2.webp ├── latex_3.webp ├── latex_4.webp ├── linear_non_linear.png ├── logo.png ├── markdown2pdf.jpg ├── ml_history.png ├── model_zoo.png ├── modularization.jpg ├── moores_law.jpg ├── no_padding_strides.gif ├── non_convex_optimization.jpeg ├── non_linear_activation_benchmark.png ├── normalization.png ├── oneflow_arch.jpeg ├── optimizar_compare.gif ├── overfitting_2.png ├── parallel.jpg ├── pooling.gif ├── pytorch_gpu.png ├── resnet_50_benchmark.jpg ├── resnet_arch.png ├── rgb_cov1.gif ├── rgb_cov2.gif ├── semantics_segmentation.gif ├── sigmoid.png ├── sougou_cloud.jpg ├── soup.jpg ├── test_img.png ├── threadripper.jpg ├── thumb_photoshop-tutorial-r-separate-an-image-into-rgb-color-layers-57693846.png ├── train_wd_0_epoch_300.png ├── train_wd_10_epoch_300.png ├── train_wd_20_epoch_300.png ├── train_wd_5_epoch_300.png ├── vc.png ├── vgg.png ├── vgg_block.svg ├── why_deep_works.jpg ├── zn_abc.jpg ├── 多层感知机.jpg ├── 屏幕截图 2021-12-24 170823.png ├── 微信截图_20211216204557.png ├── 微信截图_20211216204706.png ├── 微信截图_20211216204749.png ├── 微信截图_20211216210434.png ├── 微信截图_20211217154852.png ├── 微信截图_20211221142847.png ├── 微信截图_20211221174519.png ├── 微信截图_20211229223931.png ├── 微信截图_20211229230829.png ├── 感知机.jfif ├── 曲线.png ├── 有隐藏层的感知机.gif ├── 栗子.png ├── 欠拟合.png ├── 正常拟合.png ├── 番01-01.png ├── 番01-02.png ├── 番01-03.png ├── 示意图.png ├── 边界框预测.png ├── 过拟合.png ├── 预测结果.png └── 高维度张量运算.png ├── LICENSE ├── README.md ├── _coverpage.md ├── _sidebar.md ├── extra ├── README.md ├── 番外01-矩阵运算的广播原理.md ├── 番外02-使用和购买GPU.md ├── 番外03-Linux常用命令.md └── 番外04-Kaggle竞赛实践经验.md └── index.html /LICENSE: -------------------------------------------------------------------------------- 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