深度学习Pytorch实战
├──1.深度学习框架介绍| └──1.lesson1-PyTorch介绍.mp448.66M
├──10.卷积神经网络CNN
| ├──50.lesson37-什么是卷积-1.mp462.76M
| ├──51.lesson37-什么是卷积-2.mp439.60M
| ├──52.lesson38-卷积神经网络-1.mp441.36M
| ├──53.lesson38-卷积神经网络-2.mp462.89M
| ├──54.lesson38-卷积神经网络-3.mp435.46M
| ├──55.lesson39-Pooling&upsample.mp434.05M
| ├──56.lesson40-BatchNorm-1.mp441.45M
| ├──57.lesson40-BatchNorm-2.mp451.27M
| ├──58.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp449.28M
| ├──59.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp440.38M
| ├──60.lesson42-ResNet，DenseNet-1.mp453.18M
| ├──61.lesson42-ResNet, DenseNet-2.mp443.56M
| ├──62.lesson43-nn.Module-1.mp444.98M
| ├──63.lesson43-nn.Module-2.mp431.43M
| └──64.lesson44-数据增强Data Argumentation.mp446.85M
├──11.CIFAR10与ResNet实战
├──12.循环神经网络RNN&LSTM
| ├──65.lesson46-时间序列表示.mp453.55M
| ├──66.lesson47-RNN原理-1.mp428.41M
| ├──67.lesson47-RNN原理-2.mp434.94M
| ├──68.lesson48-RNN Layer使用-1.mp434.23M
| ├──69.lesson48-RNN Layer使用-2.mp429.91M
| ├──70.lesson49-时间序列预测.mp453.30M
| ├──71.lesson50-RNN训练难题.mp455.00M
| ├──72.lesson51-LSTM原理-1.mp432.97M
| ├──73.lesson51-LSTM原理-2.mp445.70M
| ├──74.lesson52-LSTM Layer使用.mp428.45M
| └──75.lesson53-情感分类实战.mp468.58M
├──13.对抗生成网络GAN
| ├──76.lesson54-数据分布.mp417.44M
| ├──77.lesson55-画家的成长历程.mp428.85M
| ├──78.lesson56-GAN发展.mp423.03M
| ├──79.lesson57-纳什均衡-D.mp420.42M
| ├──80.lesson58-纳什均衡-G.mp436.65M
| ├──81.lesson59-JS散度的弊端.mp436.81M
| ├──82.lesson60-EM距离.mp417.16M
| ├──83.lesson61-WGAN与WGAN-GP.mp428.84M
| ├──84.lesson62-G和D实现.mp417.28M
| ├──85.lesson63-GAN实战.mp433.30M
| ├──86.lesson64-GAN训练不稳定.mp420.20M
| └──87.lesson65-WGAN-GP实战.mp436.27M
├──2.开发环境准备
| └──2.lesson2-开发环境准备.mp454.47M
├──3.初见深度学习
| ├──3.lesson3-初探Linear Regression案例-1.mp471.93M
| ├──4.lesson3-初探Linear Regression案例-2.mp443.15M
| ├──5.lesson4-PyTorch求解Linear Regression案例.mp435.72M
| ├──6.lesson5 -手写数字问题引入1.mp436.74M
| └──7.lesson5 -手写数字问题引入2.mp421.03M
├──4.Pytorch张量操作
| ├──10.lesson7 创建Tensor 1.mp451.58M
| ├──11.lesson7 创建Tensor 2.mp444.27M
| ├──12.lesson8 索引与切片1.mp447.24M
| ├──13.lesson8 索引与切片2.mp445.41M
| ├──14.lesson9 维度变换1.mp433.07M
| ├──15.lesson9 维度变换2.mp440.69M
| ├──16.lesson9 维度变换3.mp440.76M
| ├──17.lesson9 维度变换4.mp440.80M
| ├──8.lesson6 基本数据类型1.mp454.35M
| └──9.lesson6 基本数据类型2.mp428.20M
├──5.张量高阶操作
| ├──18.lesson10 Broatcasting 1.mp457.86M
| ├──19.lesson10 Broatcasting 2.mp446.18M
| ├──20.lesson11 合并与切割1.mp446.78M
| ├──21.lesson11 合并与切割2.mp430.81M
| ├──22.lesson12 基本运算.mp467.11M
| ├──23.lesson13 数据统计1.mp439.94M
| ├──24.lesson13 数据统计2.mp454.70M
| └──25.lesson14 高阶OP.mp461.86M
├──6.随机梯度下降
| ├──26.lesson16 什么是梯度1.mp469.17M
| ├──27.lesson16 什么是梯度2.mp443.31M
| ├──28.lesson17 常见梯度.mp418.38M
| ├──29.lesson18 激活函数及其梯度1【IT会员免费+VX：DS369333】.mp445.53M
| ├──30.lesson18 激活函数及其梯度2【IT会员免费+VX：DS369333】.mp444.38M
| └──31.lesson18 激活函数及其梯度3【IT会员免费+VX：DS369333】.mp465.34M
├──7.感知机梯度传播推导
| ├──32.lesson19 单一输出感知机1.mp447.44M
| ├──33.lesson19 多输出Loss层2.mp449.69M
| ├──34.lesson20 链式法则.mp439.94M
| ├──35.lesson21 反向传播.mp482.01M
| └──36.lesson22 优化小实例.mp439.18M
├──8.多层感知机与分类器
| ├──37.lesson24 Logistic Regression.mp447.84M
| ├──38.lesson25 交叉熵.mp472.77M
| ├──39.lesson26 多分类实战.mp435.00M
| ├──40.lesson27 全连接层.mp452.13M
| ├──41.lesson28 激活函数与GPU加速【IT会员免费+VX：DS369333】.mp439.59M
| ├──42.lesson29 测试.mp453.80M
| └──43.lesson30-Visdom可视化.mp452.77M
├──9.过拟合
| ├──44.lesson31-过拟合与欠拟合.mp442.48M
| ├──45.lesson32-Train-Val-Test-交叉验证-1.mp445.95M
| ├──46.lesson32-Train-Val-Test-交叉验证-2.mp432.32M
| ├──47.lesson33-regularization.mp439.04M
| ├──48.lesson34-动量与lr衰减.mp451.46M
| └──49.lesson35-early stopping, dropout, sgd.mp451.23M
└──zfdev_tree.txt6.12kb
**** Hidden Message *****
66666666666666 深度学习Pytorch实战:victory: 看看这个怎么样感谢楼主分享谢谢了 深度学习Pytorch实战 感谢楼主分享 深度学习Pytorch实战 深度学习Pytorch实战 深度学习Pytorch实战
深度学习Pytorch实战