📁 01.深度学习框架介绍
📄 1.lesson1-PyTorch介绍.mp4 (48.7 MB)
📁 02.开发环境准备
📄 2.lesson2-开发环境准备.mp4 (54.5 MB)
📁 03.初见深度学习
📄 3.lesson3-初探Linear Regression案例-1.mp4 (71.9 MB)
📄 4.lesson3-初探Linear Regression案例-2.mp4 (43.1 MB)
📄 5.lesson4-PyTorch求解Linear Regression案例.mp4 (35.7 MB)
📄 6.lesson5 -手写数字问题引入1.mp4 (36.7 MB)
📄 7.lesson5 -手写数字问题引入2.mp4 (21.0 MB)
📁 04.Pytorch张量操作
📄 08.lesson6 基本数据类型1.mp4 (54.4 MB)
📄 09.lesson6 基本数据类型2.mp4 (28.2 MB)
📄 10.lesson7 创建Tensor 1.mp4 (51.6 MB)
📄 11.lesson7 创建Tensor 2.mp4 (44.3 MB)
📄 12.lesson8 索引与切片1.mp4 (47.2 MB)
📄 13.lesson8 索引与切片2.mp4 (45.4 MB)
📄 14.lesson9 维度变换1.mp4 (33.1 MB)
📄 15.lesson9 维度变换2.mp4 (40.7 MB)
📄 16.lesson9 维度变换3.mp4 (40.8 MB)
📄 17.lesson9 维度变换4.mp4 (40.8 MB)
📁 05.张量高阶操作
📄 18.lesson10 Broatcasting 1.mp4 (57.9 MB)
📄 19.lesson10 Broatcasting 2.mp4 (46.2 MB)
📄 20.lesson11 合并与切割1.mp4 (46.8 MB)
📄 21.lesson11 合并与切割2.mp4 (30.8 MB)
📄 22.lesson12 基本运算.mp4 (67.1 MB)
📄 23.lesson13 数据统计1.mp4 (39.9 MB)
📄 24.lesson13 数据统计2.mp4 (54.7 MB)
📄 25.lesson14 高阶OP.mp4 (61.9 MB)
📁 06.随机梯度下降
📄 26.lesson16 什么是梯度1.mp4 (69.2 MB)
📄 27.lesson16 什么是梯度2.mp4 (43.3 MB)
📄 28.lesson17 常见梯度.mp4 (18.4 MB)
📄 29.lesson18 激活函数及其梯度1.mp4 (45.5 MB)
📄 30.lesson18 激活函数及其梯度2.mp4 (44.4 MB)
📄 31.lesson18 激活函数及其梯度3.mp4 (65.3 MB)
📁 07.感知机梯度传播推导
📄 32.lesson19 单一输出感知机1.mp4 (47.4 MB)
📄 33.lesson19 多输出Loss层2.mp4 (49.7 MB)
📄 34.lesson20 链式法则.mp4 (39.9 MB)
📄 35.lesson21 反向传播.mp4 (82.0 MB)
📄 36.lesson22 优化小实例.mp4 (39.2 MB)
📁 08.多层感知机与分类器
📄 37.lesson24 Logistic Regression.mp4 (47.8 MB)
📄 38.lesson25 交叉熵.mp4 (72.8 MB)
📄 39.lesson26 多分类实战.mp4 (35.0 MB)
📄 40.lesson27 全连接层.mp4 (52.1 MB)
📄 41.lesson28 激活函数与GPU加速.mp4 (39.6 MB)
📄 42.lesson29 测试.mp4 (53.8 MB)
📄 43.lesson30-Visdom可视化.mp4 (52.8 MB)
📁 09.过拟合
📄 44.lesson31-过拟合与欠拟合.mp4 (42.5 MB)
📄 45.lesson32-Train-Val-Test-交叉验证-1.mp4 (45.9 MB)
📄 46.lesson32-Train-Val-Test-交叉验证-2.mp4 (32.3 MB)
📄 47.lesson33-regularization.mp4 (39.0 MB)
📄 48.lesson34-动量与lr衰减.mp4 (51.5 MB)
📄 49.lesson35-early stopping, dropout, sgd.mp4 (51.2 MB)
📁 10.卷积神经网络CNN
📄 50.lesson37-什么是卷积-1.mp4 (62.8 MB)
📄 51.lesson37-什么是卷积-2.mp4 (39.6 MB)
📄 52.lesson38-卷积神经网络-1.mp4 (41.4 MB)
📄 53.lesson38-卷积神经网络-2.mp4 (62.9 MB)
📄 54.lesson38-卷积神经网络-3.mp4 (35.5 MB)
📁 11.CIFAR10与ResNet实战
📄 55.lesson39-Pooling&upsample.mp4 (34.1 MB)
📄 56.lesson40-BatchNorm-1.mp4 (41.4 MB)
📄 57.lesson40-BatchNorm-2.mp4 (51.3 MB)
📄 58.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp4 (49.3 MB)
📄 59.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp4 (40.4 MB)
📄 60.lesson42-ResNet，DenseNet-1.mp4 (53.2 MB)
📄 61.lesson42-ResNet, DenseNet-2.mp4 (43.6 MB)
📄 62.lesson43-nn.Module-1.mp4 (45.0 MB)
📄 63.lesson43-nn.Module-2.mp4 (31.4 MB)
📄 64.lesson44-数据增强Data Argumentation.mp4 (46.8 MB)
📁 12.循环神经网络RNN&LSTM
📄 65.lesson46-时间序列表示.mp4 (53.5 MB)
📄 66.lesson47-RNN原理-1.mp4 (28.4 MB)
📄 67.lesson47-RNN原理-2.mp4 (34.9 MB)
📄 68.lesson48-RNN Layer使用-1.mp4 (34.2 MB)
📄 69.lesson48-RNN Layer使用-2.mp4 (29.9 MB)
📄 70.lesson49-时间序列预测.mp4 (53.3 MB)
📄 71.lesson50-RNN训练难题.mp4 (55.0 MB)
📄 72.lesson51-LSTM原理-1.mp4 (33.0 MB)
📄 73.lesson51-LSTM原理-2.mp4 (45.7 MB)
📄 74.lesson52-LSTM Layer使用.mp4 (28.4 MB)
📄 75.lesson53-情感分类实战.mp4 (68.6 MB)
📁 13.对抗生成网络GAN
📄 76.lesson54-数据分布.mp4 (17.4 MB)
📄 77.lesson55-画家的成长历程.mp4 (28.9 MB)
📄 78.lesson56-GAN发展.mp4 (23.0 MB)
📄 79.lesson57-纳什均衡-D.mp4 (20.4 MB)
📄 80.lesson58-纳什均衡-G.mp4 (36.6 MB)
📄 81.lesson59-JS散度的弊端.mp4 (36.8 MB)
📄 82.lesson60-EM距离.mp4 (17.2 MB)
📄 83.lesson61-WGAN与WGAN-GP.mp4 (28.8 MB)
📄 84.lesson62-G和D实现.mp4 (17.3 MB)
📄 85.lesson63-GAN实战.mp4 (33.3 MB)
📄 86.lesson64-GAN训练不稳定.mp4 (20.2 MB)
📄 87.lesson65-WGAN-GP实战.mp4 (36.3 MB)
📄 zfdev_tree.txt (6.1 KB)
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