详细目录：
1 [2.40G]
10 [211.75M]
10-1 rnn网络基础_.mp4 [15.84M]
10-2 rnn常见网络结构-simple rnn网络_.mp4 [23.72M]
10-3 bi-rnn网络_.mp4 [9.59M]
10-4 lstm网络基础_.mp4 [34.38M]
10-5 attention结构_.mp4 [37.22M]
10-6 transformer结构_.mp4 [33.28M]
10-7 bert结构_.mp4 [25.66M]
10-8 nlp基础概念介绍_.mp4 [32.06M]
11 [809.81M]
11-1 文本情感分析-情感分类概念介绍_.mp4 [23.50M]
11-10 文本情感分类-test脚本定义_.mp4 [63.38M]
11-2 文本情感分类关键流程介绍_.mp4 [3.75M]
11-3 文本情感分类之文本预处理_.mp4 [23.69M]
11-4 文本情感分类之特征提取与文本表示_.mp4 [39.44M]
11-5 文本情感分类之深度学习模型_.mp4 [36.81M]
11-6 文本情感分类-数据准备_.mp4 [184.69M]
11-7 文本情感分类-dataset类定义_.mp4 [129.09M]
11-8 文本情感分类-model类定义_.mp4 [122.59M]
11-9 文本情感分类-train脚本定义_.mp4 [182.88M]
12 [1.00G]
12-1 机器翻译相关方法-应用场景-评价方法_.mp4 [49.44M]
12-2 seq2seq-attention编程实例数据准备-模型结构-相关函数_.mp4 [20.34M]
12-3 seq2seq-attention编程实例-定义数据处理模块_.mp4 [175.03M]
12-4 seq2seq-attention编程实例-定义模型结构模块（上）_.mp4 [140.69M]
12-5 seq2seq-attention编程实例-定义模型结构模块（下）_.mp4 [157.56M]
12-6 seq2seq-attention编程实例-定义train模块（上）_.mp4 [103.91M]
12-7 seq2seq-attention编程实例-定义train模块（下）_.mp4 [107.31M]
12-8 seq2seq-attention编程实例-定义train模块-loss function_.mp4 [179.47M]
12-9 seq2seq-attention编程实例-定义eval模块_.mp4 [90.59M]
13 [90.25M]
13-1 pytorch模型开发与部署基础平台介绍_.mp4 [15.75M]
13-2 pytorch工程化基础–torchscript_.mp4 [24.34M]
13-3 pytorch服务端发布平台–torchserver_.mp4 [22.91M]
13-4 pytorch终端推理基础–onnx_.mp4 [27.25M]
14 [62.31M]
14-1 linux操作基础串讲_.mp4 [62.31M]
15 [29.50M]
15-1 课程总结_.mp4 [29.50M]
1-1 课程导学~1_ (2).mp4 [229.43M]
2 [157.41M]
2-1 初识pytorch基本框架_.mp4 [18.34M]
2-2 环境配置（1）_.mp4 [22.63M]
2-3 环境配置（2）_.mp4 [116.44M]
3 [1.62G]
3-1 机器学习中的分类与回归问题-机器学习基本构成元素_.mp4 [27.28M]
3-10 取整-余_.mp4 [26.13M]
3-11 比较运算-排序-topk-kthvalue-数据合法性校验_.mp4 [155.72M]
3-12 三角函数_.mp4 [23.59M]
3-13 其他数学函数_.mp4 [15.06M]
3-14 pytorch与统计学方法_.mp4 [77.81M]
3-15 pytorch与分布函数_.mp4 [11.13M]
3-16 pytorch与随机抽样_.mp4 [21.19M]
3-17 pytorch与线性代数运算_.mp4 [60.44M]
3-18 pytorch与矩阵分解-pca_.mp4 [50.41M]
3-19 pytorch与矩阵分解-svd分解-lda_.mp4 [28.00M]
3-2 tensor的基本定义_.mp4 [12.25M]
3-20 pytorch与张量裁剪_.mp4 [32.22M]
3-21 pytorch与张量的索引与数据筛选_.mp4 [179.47M]
3-22 pytorch与张量组合与拼接_.mp4 [58.25M]
3-23 pytorch与张量切片_.mp4 [56.34M]
3-24 pytorch与张量变形_.mp4 [113.75M]
3-25 pytorch与张量填充_.mp4 [9.84M]
3-26 pytorch与傅里叶变换_.mp4 [1.81M]
3-27 pytorch简单编程技巧_.mp4 [53.59M]
3-28 pytorch与autograd-导数-方向导数-偏导数-梯度的概念_.mp4 [16.56M]
3-29 pytorch与autograd-梯度与机器学习最优解_.mp4 [25.97M]
3-3 tensor与机器学习的关系_.mp4 [13.09M]
3-30 pytorch与autograd-variable$tensor_.mp4 [4.53M]
3-31 pytorch与autograd-如何计算梯度_.mp4 [5.09M]
3-32 pytorch与autograd中的几个重要概念-variable-grad-grad_fn_.mp4 [22.19M]
3-33 pytorch与autograd中的几个重要概念-function_.mp4 [59.09M]
3-34 pytorch与nn库_.mp4 [50.88M]
3-35 pytorch与visdom_.mp4 [14.28M]
3-36 pytorch与tensorboardx_.mp4 [23.78M]
3-37 pytorch与torchvision_.mp4 [12.53M]
3-4 tensor创建编程实例_.mp4 [116.78M]
3-5 tensor的属性_.mp4 [14.34M]
3-6 tensor的属性-稀疏的张量的编程实践_.mp4 [52.25M]
3-7 tensor的算术运算_.mp4 [18.06M]
3-8 tensor的算术运算编程实例_.mp4 [153.56M]
3-9 in-place的概念和广播机制_.mp4 [40.31M]
4 [800.88M]
4-1 机器学习和神经网络的基本概念（1）_.mp4 [77.84M]
4-2 机器学习和神经网络的基本概念（2）_.mp4 [52.19M]
4-3 利用神经网络解决分类和回归问题（1）_.mp4 [82.22M]
4-4 利用神经网络解决分类和回归问题（2）_.mp4 [228.41M]
4-5 利用神经网络解决分类和回归问题（3）_.mp4 [94.56M]
4-6 利用神经网络解决分类和回归问题（4）_.mp4 [107.06M]
4-7 利用神经网络解决分类和回归问题（5）_.mp4 [158.59M]
5 [538.31M]
5-1 计算机视觉基本概念_.mp4 [91.31M]
5-10 多分支网络结构_.mp4 [9.13M]
5-11 attention的网络结构_.mp4 [37.38M]
5-12 学习率_.mp4 [13.72M]
5-13 优化器_.mp4 [21.34M]
5-14 卷积神经网添加正则化_.mp4 [7.00M]
5-2 图像处理常见概念_.mp4 [106.16M]
5-3 特征工程_.mp4 [37.75M]
5-4 卷积神经网（上）_.mp4 [49.03M]
5-5 卷积神经网（下）_.mp4 [45.09M]
5-6 pooling层_.mp4 [14.56M]
5-7 激活层-bn层-fc层-损失层_.mp4 [36.94M]
5-8 经典卷积神经网络结构_.mp4 [39.72M]
5-9 轻量型网络结构_.mp4 [29.19M]
6 [1.76G]
1-1 图像分类网络模型框架解读（上）_.mp4 [25.69M]
1-10 pytorch搭建cifar10训练脚本搭建-resnet结构（下）_.mp4 [120.16M]
1-11 pytorch搭建cifar10训练脚本搭建-mobilenetv1结构_.mp4 [110.81M]
1-12 pytorch搭建cifar10训练脚本搭建-inception结构（上）_.mp4 [98.72M]
1-13 pytorch搭建cifar10训练脚本搭建-inception结构（下）_.mp4 [70.09M]
1-14 pytorch搭建cifar10训练脚本搭建-调用pytorch标准网络resnet18等_.mp4 [52.81M]
1-15 pytorch搭建cifar10推理测试脚本搭建_.mp4 [82.44M]
1-16 分类问题优化思路_.mp4 [63.00M]
1-17 分类问题最新研究进展和方向_.mp4 [48.09M]
1-2 图像分类网络模型框架解读（下）_.mp4 [41.66M]
1-3 cifar10数据介绍-读取-处理（上）_.mp4 [100.63M]
1-4 cifar10数据介绍-读取-处理（下）_.mp4 [114.25M]
1-5 pytorch自定义数据加载-加载cifar10数据_.mp4 [131.13M]
1-6 pytorch搭建 vggnet 实现cifar10图像分类_.mp4 [122.13M]
1-7 pytorch搭建cifar10训练脚本-tensorboard记录log（上）_.mp4 [149.00M]
1-8 pytorch搭建cifar10训练脚本-tensorboard记录log（下）_.mp4 [328.19M]
1-9 pytorch搭建cifar10训练脚本搭建-resnet结构（上）_.mp4 [140.13M]
7 [1.23G]
7-1 目标检测问题介绍（上）_.mp4 [56.72M]
7-10 mmdetection log分析_.mp4 [170.00M]
7-2 目标检测问题介绍（下）_.mp4 [52.69M]
7-3 pascal voc-coco数据集介绍_.mp4 [15.06M]
7-4 mmdetection框架介绍-安装说明_.mp4 [43.31M]
7-5 mmdetection框架使用说明_.mp4 [51.75M]
7-6 mmdetection训练passcal voc目标检测任务（上）_.mp4 [290.97M]
7-7 mmdetection训练passcal voc目标检测任务（中）_.mp4 [236.06M]
7-8 mmdetection训练passcal voc目标检测任务（下）_.mp4 [286.19M]
7-9 mmdetection test脚本_.mp4 [55.78M]
8 [1.02G]
8-1 图像分割基本概念_.mp4 [36.81M]
8-2 图像分割方法介绍_.mp4 [54.16M]
8-3 图像分割评价指标及目前面临的挑战_.mp4 [23.25M]
8-4 coco数据集介绍_.mp4 [13.75M]
8-5 detectron框架介绍和使用简单说明_.mp4 [28.34M]
8-6 coco数据集标注文件解析_.mp4 [84.97M]
8-7 detectron源码解读和模型训练-demo测试_.mp4 [800.16M]
9 [904.00M]
9-1 gan的基础概念和典型模型介绍（上）_.mp4 [34.28M]
9-2 gan的基础概念和典型模型介绍（下）_.mp4 [57.47M]
9-3 图像风格转换数据下载与自定义dataset类_.mp4 [120.69M]
9-4 cyclegan模型搭建-model_.mp4 [142.75M]
9-5 cyclegan模型搭建-train（上）_.mp4 [198.31M]
9-6 cyclegan模型搭建-train（下）_.mp4 [284.16M]
9-7 cyclegan模型搭建-test_.mp4 [66.34M]
资料 [143.33M]
资料.zip [143.33M]
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