├─第1章 课程介绍-选择Pytorch的理由
│&nBSp; &nBSp;&nBSp; &nBSp;1-1 课程导学.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第2章 初识PyTorch框架与环境搭建
│&nBSp; &nBSp;&nBSp; &nBSp;2-1 初识Pytorch基本框架.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;2-2 环境配置（1）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;2-3 环境配置（2）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第3章 PyTorch入门基础串讲
│&nBSp; &nBSp;&nBSp; &nBSp;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
│&nBSp; &nBSp;&nBSp; &nBSp;3-2 Tensor的基本定义.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-3 Tensor与机器学习的关系.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-3 Tensor与机器学习的关系.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-4 Tensor创建编程实例.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-5 Tensor的属性.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-6 Tensor的属性-稀疏的张量的编程实践.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-7 Tensor的算术运算.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-8 Tensor的算术运算编程实例.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-9 in-place的概念和广播机制.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-10 取整-余.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-11 比较运算-排序-TOPk-kthvalue-数据合法性校验.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-12 三角函数.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-13 其他数学函数.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-14 Pytorch与统计学方法.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-15 Pytorch与分布函数.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-16 Pytorch与随机抽样.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-17 Pytorch与线性代数运算.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-18 Pytorch与矩阵分解-PCA.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-19 Pytorch与矩阵分解-SVD分解-LDA.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-20 Pytorch与张量裁剪.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-21 Pytorch与张量的索引与数据筛选.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-22 Pytorch与张量组合与拼接.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-23 Pytorch与张量切片.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-24 Pytorch与张量变形.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-25 Pytorch与张量填充.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-26 Pytorch与傅里叶变换.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-27 Pytorch简单编程技巧.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-28 Pytorch与autogrAD-导数-方向导数-偏导数-梯度的概念.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-29 Pytorch与autogrAD-梯度与机器学习最优解.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-30 Pytorch与autogrAD-Variable$tensor.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-31 Pytorch与autogrAD-如何计算梯度.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-32 Pytorch与autogrAD中的几个重要概念-variable-grAD-grAD_fn.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-33 Pytorch与autogrAD中的几个重要概念-function.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-34 Pytorch与nn库.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-35 Pytorch与viSDom.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-36 Pytorch与tensorboardX.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;3-37 Pytorch与torchvision.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第4章 PyTorch搭建简单神经网络
│&nBSp; &nBSp;&nBSp; &nBSp;4-1 机器学习和神经网络的基本概念（1）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-2 机器学习和神经网络的基本概念（2）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-3 利用神经网络解决分类和回归问题（1）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-4 利用神经网络解决分类和回归问题（2）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-5 利用神经网络解决分类和回归问题（3）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-6 利用神经网络解决分类和回归问题（4）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;4-7 利用神经网络解决分类和回归问题（5）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第5章 计算机视觉与卷积神经网络基础串讲
│&nBSp; &nBSp;&nBSp; &nBSp;5-1 计算机视觉基本概念.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-2 图像处理常见概念.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-3 特征工程.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-4 卷积神经网（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-5 卷积神经网（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-6 pooling层.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-7 激活层-BN层-FC层-损失层.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-8 经典卷积神经网络结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-9 轻量型网络结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-10 多分支网络结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-11 attention的网络结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-12 学习率.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-13 优化器.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;5-14 卷积神经网添加正则化.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第6章 PyTorch实战计算机视觉任务-CifaR10图像分类
│&nBSp; &nBSp;&nBSp; &nBSp;1-1 图像分类网络模型框架解读（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-2 图像分类网络模型框架解读（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-3 cifaR10数据介绍-读取-处理（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-4 cifaR10数据介绍-读取-处理（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-5 PyTorch自定义数据加载-加载CifaR10数据.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-6 PyTorch搭建 VGGNet 实现CifaR10图像分类.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-7 PyTorch搭建cifaR10训练脚本-tensorboard记录LOG（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-8 PyTorch搭建cifaR10训练脚本-tensorboard记录LOG（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-9 PyTorch搭建cifaR10训练脚本搭建-ResNet结构（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-10 PyTorch搭建cifaR10训练脚本搭建-ResNet结构（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-11 PyTorch搭建cifaR10训练脚本搭建-Mobilenetv1结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-12 PyTorch搭建cifaR10训练脚本搭建-Inception结构（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-13 PyTorch搭建cifaR10训练脚本搭建-Inception结构（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-14 PyTorch搭建cifaR10训练脚本搭建-调用Pytorch标准网络ResNet18等.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-15 PyTorch搭建cifaR10推理测试脚本搭建.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-16 分类问题优化思路.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;1-17 分类问题最新研究进展和方向.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第7章 Pytorch实战计算机视觉任务-Pascal VOC目标检测问题
│&nBSp; &nBSp;&nBSp; &nBSp;7-1 目标检测问题介绍（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-2 目标检测问题介绍（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-3 Pascal VOC-COCO数据集介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-4 MMdetection框架介绍-安装说明.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-5 MMdetection框架使用说明.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-6 MMdetection训练Passcal VOC目标检测任务（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-7 MMdetection训练Passcal VOC目标检测任务（中）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-8 MMdetection训练Passcal VOC目标检测任务（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-9 MMdetection Test脚本.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;7-10 MMdetection LOG分析.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第8章 PyTorch实战计算机视觉任务-COCO目标分割问题
│&nBSp; &nBSp;&nBSp; &nBSp;8-1 图像分割基本概念.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-2 图像分割方法介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-3 图像分割评价指标及目前面临的挑战.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-4 COCO数据集介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-5 detectron框架介绍和使用简单说明.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-6 coco数据集标注文件解析.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;8-7 detectron源码解读和模型训练-dEMO测试.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第9章 PyTorch搭建GAN网络实战图像风格迁移
│&nBSp; &nBSp;&nBSp; &nBSp;9-1 GAN的基础概念和典型模型介绍（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-2 GAN的基础概念和典型模型介绍（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-3 图像风格转换数据下载与自定义dataset类.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-4 cycleGAN模型搭建-model.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-5 cycleGAN模型搭建-trAIn（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-6 cycleGAN模型搭建-trAIn（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;9-7 cycleGAN模型搭建-test.mp4
│&nBSp;&nBSp;
├─第10章 循环神经网与NLP基础串讲
│&nBSp; &nBSp;&nBSp; &nBSp;10-1 RNN网络基础.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-2 RNN常见网络结构-sIMple RNN网络.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-3 Bi-RNN网络.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-4 LSTM网络基础.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-5 Attention结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-6 Transformer结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-7 BERT结构.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;10-8 NLP基础概念介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第11章 PyTorch实战中文文本情感分类问题
│&nBSp; &nBSp;&nBSp; &nBSp;11-1 文本情感分析-情感分类概念介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-2 文本情感分类关键流程介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-3 文本情感分类之文本预处理.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-4 文本情感分类之特征提取与文本表示.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-5 文本情感分类之深度学习模型.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-6 文本情感分类-数据准备.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-7 文本情感分类-dataset类定义.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-8 文本情感分类-model类定义.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-9 文本情感分类-trAIn脚本定义.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;11-10 文本情感分类-test脚本定义.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第12章 PyTorch实战机器翻译问题
│&nBSp; &nBSp;&nBSp; &nBSp;12-1 机器翻译相关方法-应用场景-评价方法.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-2 Seq2Seq-Attention编程实例数据准备-模型结构-相关函数.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-3 Seq2Seq-Attention编程实例-定义数据处理模块.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-4 Seq2Seq-Attention编程实例-定义模型结构模块（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-5 Seq2Seq-Attention编程实例-定义模型结构模块（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-6 Seq2Seq-Attention编程实例-定义trAIn模块（上）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-7 Seq2Seq-Attention编程实例-定义trAIn模块（下）.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-8 Seq2Seq-Attention编程实例-定义trAIn模块-loss function.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;12-9 Seq2Seq-Attention编程实例-定义eval模块.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第13章 PyTorch工程应用介绍
│&nBSp; &nBSp;&nBSp; &nBSp;13-1 PyTorch模型开发与部署基础平台介绍.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;13-2 PyTorch工程化基础--TorchscrIPt.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;13-3 PyTorch服务端发布平台--Torchserver.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;13-4 PyTorch终端推理基础--ONNX.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第14章 【选修】linux操作基础串讲
│&nBSp; &nBSp;&nBSp; &nBSp;14-1 linux操作基础串讲.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
├─第15章 课程总结与回顾
│&nBSp; &nBSp;&nBSp; &nBSp;15-1 课程总结.mp4
│&nBSp; &nBSp;&nBSp; &nBSp;
└─课程资料.zIP

*声明：课程资源购自网络，版权归原作者所有，仅供参考学习使用，严禁外传及商用，若侵犯到您的权益请联系客服删除。