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01.Lesson-0.1-GPU购买与GPU白嫖指南.mp4 |
2023-12-31 05:45:20 |
142.26MB |
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02.Lesson-0.2-PyTorch安装与部署（CPU版本）.mp4 |
2023-12-31 05:45:20 |
85.71MB |
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03.Lesson-0.3-PyTorch安装与配置（GPU版本）.mp4 |
2023-12-31 05:45:20 |
133.96MB |
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04.Lesson-1-张量的创建与常用方法.mp4 |
2023-12-31 05:45:20 |
404.65MB |
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05.Lesson-2-张量的索引、分片、合并及维度调整.mp4 |
2023-12-31 05:45:20 |
305.24MB |
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06.Lesson-3-张量的广播和科学运算.mp4 |
2023-12-31 05:45:20 |
312.28MB |
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07.Lesson-4-张量的线性代数运算.mp4 |
2023-12-31 05:45:20 |
407.08MB |
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08.Lesson-5-基本优化方法与最小二乘法.mp4 |
2023-12-31 05:45:20 |
1.51GB |
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09.Lesson-6-动态计算图与梯度下降入门.mp4 |
2023-12-31 05:45:20 |
1.46GB |
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10.Lesson-7.1-神经网络的诞生与发展.mp4 |
2023-12-31 05:45:20 |
217.83MB |
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100.Lesson-18.3.1-数据探索（上）：数据结构与病理图像可视化.mp4 |
2023-12-31 05:45:20 |
385.25MB |
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101.Lesson-18.3.2-数据探索（下）：标签探索与恶性率可视化.mp4 |
2023-12-31 05:45:20 |
287.39MB |
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102.Lesson-18.4.1-自定义数据集导入类与数据集分割.mp4 |
2023-12-31 05:45:20 |
278.20MB |
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103.Lesson-18.4.2.1-医疗数据的数据增强-(1)-10项色彩增强手段.mp4 |
2023-12-31 05:45:20 |
322.44MB |
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104.Lesson-18.4.2.2-医疗数据的数据增强-(2)-生成对抗网络与染色标准化.mp4 |
2023-12-31 05:45:20 |
363.83MB |
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105.Lesson-18.4.3.1-实现色彩增强-(1)-认识imgaug与skimage.mp4 |
2023-12-31 05:45:20 |
180.35MB |
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106.Lesson-18.4.3.2-实现色彩增强-(2)-imgaug中的仿射变换与随机增强.mp4 |
2023-12-31 05:45:20 |
210.54MB |
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107.Lesson-18.4.3.3-实现色彩增强-(3)-imgaug中的线性变换与色彩加乘.mp4 |
2023-12-31 05:45:20 |
238.44MB |
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108.Lesson-18.4.3.4-实现色彩增强-(4)-基于苏木素H与伊红E的色彩空间转换.mp4 |
2023-12-31 05:45:20 |
228.03MB |
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109.Lesson-18.4.3.5-数据增强方案-(5)：imgaug与skimage嵌入运行.mp4 |
2023-12-31 05:45:20 |
199.49MB |
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11.Lesson-7.2-机器学习中的基本概念.mp4 |
2023-12-31 05:45:20 |
509.35MB |
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110.Lesson-18.4.3.6-数据增强方案-(6)：基于HED通道的单通道操作嵌入运行.mp4 |
2023-12-31 05:45:20 |
220.36MB |
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111.Lesson-18.4.4.1-生成对抗网络的基本原理与损失函数.mp4 |
2023-12-31 05:45:20 |
292.32MB |
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112.Lesson-18.4.4.2-(1)-从0实现GAN的反向传播与训练.mp4 |
2023-12-31 05:45:20 |
271.91MB |
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113.Lesson-18.4.4.2-(2)-判别器的反向传播.mp4 |
2023-12-31 05:45:20 |
201.91MB |
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114.Lesson-18.4.4.2-(3)-生成器的反向传播.mp4 |
2023-12-31 05:45:20 |
318.70MB |
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115.Lesson-18.4.4.3-转置卷积层与DCGAN(1)：基本原理与实现.mp4 |
2023-12-31 05:45:20 |
278.43MB |
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116.Lesson-18.4.4.3-转置卷积层与DCGAN(2)：带步长与填充的转置卷积层（上.mp4 |
2023-12-31 05:45:20 |
137.07MB |
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117.Lesson-18.4.4.3-转置卷积层与DCGAN(2)：带步长与填充的转置卷积层（下.mp4 |
2023-12-31 05:45:20 |
174.28MB |
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118.Lesson-18.4.4.3-转置卷积层与DCGAN(3)：DCGAN架构复现-(上).mp4 |
2023-12-31 05:45:20 |
176.00MB |
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119.Lesson-18.4.4.3-转置卷积层与DCGAN(3)：DCGAN架构复现-(下).mp4 |
2023-12-31 05:45:20 |
143.23MB |
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12.Lesson-7.3-深入理解PyTorch框架.mp4 |
2023-12-31 05:45:20 |
411.52MB |
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120.Lesson-18.4.4.3-转置卷积层与DCGAN(4)：从DCGAN到pix2pix.mp4 |
2023-12-31 05:45:20 |
66.02MB |
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121.Lesson-18.4.4.4-cGAN与InfoGAN(1)：基本运行原理.mp4 |
2023-12-31 05:45:20 |
222.81MB |
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122.Lesson-18.4.4.4-cGAN与InfoGAN(2)-标签输入与Embed技巧.mp4 |
2023-12-31 05:45:20 |
314.87MB |
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123.Lesson-18.4.4.4-cGAN与infoGAN-(3)-从0复现一个cGAN架构.mp4 |
2023-12-31 05:45:20 |
232.40MB |
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124.Lesson-18.4.5.1-自动编码器家族(1)：认识自动编码器.mp4 |
2023-12-31 05:45:20 |
152.73MB |
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125.Lesson-18.4.5.1-自动编码器家族(2)：三大类自动编码器.mp4 |
2023-12-31 05:45:20 |
249.38MB |
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126.Lesson-18.4.5.1-自动编码器家族(3)：自动编码器的应用场景.mp4 |
2023-12-31 05:45:20 |
172.86MB |
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127.Lesson-18.4.5.2【加餐】变分自动编码器(1)：数据流与细节梳理.mp4 |
2023-12-31 05:45:20 |
233.80MB |
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128.Lesson-18.4.5.2【加餐】变分自动编码器(2)：损失函数详解.mp4 |
2023-12-31 05:45:20 |
186.66MB |
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129.Lesson-18.4.5.2【加餐】变分自动编码器(3)：重参数化技巧.mp4 |
2023-12-31 05:45:20 |
115.78MB |
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13.Lesson-8.1单层回归神经网络-&-Tensor新手避坑指南.mp4 |
2023-12-31 05:45:20 |
799.78MB |
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130.Lesson-18.4.6.1-分割架构必备基础.mp4 |
2023-12-31 05:45:20 |
182.77MB |
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131.Lesson-18.4.6.2-Unet架构复现.mp4 |
2023-12-31 05:45:20 |
195.50MB |
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14.Lesson-8.2-torch.nn.Linear实现单层回归网络的正向传播.mp4 |
2023-12-31 05:45:20 |
318.35MB |
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15.Lesson-8.3-二分类神经网络的原理与实现.mp4 |
2023-12-31 05:45:20 |
498.14MB |
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16.Lesson-8.4-torch.nn.functional实现单层二分类网络的正向传播.mp4 |
2023-12-31 05:45:20 |
216.03MB |
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17.Lesson-8.5-多分类神经网络.mp4 |
2023-12-31 05:45:20 |
531.86MB |
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18.Lesson-9.1-从异或门问题认识多层神经网络.mp4 |
2023-12-31 05:45:20 |
732.25MB |
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19.Lesson-9.2-黑箱：深度神经网络的不可解释性.mp4 |
2023-12-31 05:45:20 |
405.00MB |
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20.Lesson-9.3-&-9.4-层与激活函数.mp4 |
2023-12-31 05:45:20 |
262.03MB |
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21.Lesson-9.5-从0实现深度神经网络的正向传播.mp4 |
2023-12-31 05:45:20 |
997.90MB |
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22.Lesson-10.1-SSE与二分类交叉熵损失.mp4 |
2023-12-31 05:45:20 |
222.57MB |
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23.Lesson-10.2-二分类交叉熵的原理与实现.mp4 |
2023-12-31 05:45:20 |
408.43MB |
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24.Lesson-10.3-多分类交叉熵的原理与实现.mp4 |
2023-12-31 05:45:20 |
487.38MB |
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25.Lesson-11.1-梯度下降中的两个关键问题.mp4 |
2023-12-31 05:45:20 |
525.79MB |
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26.Lesson-11.2.1-反向传播的原理.mp4 |
2023-12-31 05:45:20 |
505.30MB |
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27.Lesson-11.2.2-反向传播的实现.mp4 |
2023-12-31 05:45:20 |
417.51MB |
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28.Lesson-11.3-走出第一步：动量法Momentum.mp4 |
2023-12-31 05:45:20 |
655.70MB |
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29.Lesson-11.4-开始迭代：batch与epochs.mp4 |
2023-12-31 05:45:20 |
674.39MB |
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30.Lesson-11.5.1-在Fashion-MNIST数据集上实现完整的神经网络（上）.mp4 |
2023-12-31 05:45:20 |
504.54MB |
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31.Lesson-11.5.2-在Fashion-MNIST数据集上实现完整的神经网络（下）.mp4 |
2023-12-31 05:45:20 |
1,017.73MB |
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32.Lesson-12.0-深度学习基础网络手动搭建与快速实现.mp4 |
2023-12-31 05:45:20 |
236.24MB |
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33.Lesson-12.1-深度学习建模实验中数据集生成函数的创建与使用.mp4 |
2023-12-31 05:45:20 |
1.19GB |
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34.Lesson-12.2-可视化工具TensorBoard的安装与使用.mp4 |
2023-12-31 05:45:20 |
387.52MB |
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35.Lesson-12.3-线性回归建模实验.mp4 |
2023-12-31 05:45:20 |
569.75MB |
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36.Lesson-12.4-逻辑回归建模实验.mp4 |
2023-12-31 05:45:20 |
704.44MB |
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37.Lesson-12.5-softmax回归建模实验.mp4 |
2023-12-31 05:45:20 |
959.72MB |
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38.Lesson-13.1-深度学习建模目标与性能评估理论.mp4 |
2023-12-31 05:45:20 |
1.10GB |
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39.Lesson-13.2.1-模型拟合度概念介绍与欠拟合模型的结构调整策略.mp4 |
2023-12-31 05:45:20 |
1.20GB |
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40.Lesson-13.2.2-【加餐】损失函数的随机创建现象详解.mp4 |
2023-12-31 05:45:20 |
357.39MB |
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41.Lesson-13.3.1-梯度不平稳性与Glorot条件（1）.mp4 |
2023-12-31 05:45:20 |
624.94MB |
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42.Lesson-13.3.2-梯度不平稳性与Glorot条件（2）.mp4 |
2023-12-31 05:45:20 |
457.15MB |
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43.Lesson-13.3.3-梯度不平稳性与Glorot条件（3）.mp4 |
2023-12-31 05:45:20 |
514.04MB |
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44.Lesson-13.4-Dead-ReLU-Problem与学习率优化.mp4 |
2023-12-31 05:45:20 |
763.22MB |
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45.Lesson-13.5-Xavier方法与kaiming方法（HE初始化）.mp4 |
2023-12-31 05:45:20 |
919.39MB |
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46.Lesson-14.1-数据归一化与Batch-Normalization基础理论.mp4 |
2023-12-31 05:45:20 |
1.01GB |
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47.Lesson-14.2-Batch-Normalization在PyTorch中的实现.mp4 |
2023-12-31 05:45:20 |
1.16GB |
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48.Lesson-14.3-Batch-Normalization综合调参实战.mp4 |
2023-12-31 05:45:20 |
944.67MB |
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49.Lesson-15.1-学习率调度基本概念与手动实现方法.mp4 |
2023-12-31 05:45:20 |
895.10MB |
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50.Lesson-15.2-学习率调度在PyTorch中的实现方法.mp4 |
2023-12-31 05:45:20 |
885.37MB |
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51.Lesson-16.1-配置环境，计算机视觉行业综述.mp4 |
2023-12-31 05:45:20 |
216.47MB |
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52.Lesson-16.2-图像的基本操作.mp4 |
2023-12-31 05:45:20 |
579.20MB |
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53.Lesson-16.3-卷积操作与边缘检测.mp4 |
2023-12-31 05:45:20 |
431.99MB |
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54.Lesson-16.4-卷积遇见深度学习.mp4 |
2023-12-31 05:45:20 |
291.32MB |
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55.Lesson-16.5.1-在Pytorch中实现卷积网络：卷积核、输入通与特征图.mp4 |
2023-12-31 05:45:20 |
444.89MB |
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56.Lesson-16.5.2-在PyTorch中实现卷积网络：步长与填充.mp4 |
2023-12-31 05:45:20 |
621.77MB |
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57.Lesson-16.5.3-在PyTorch中实现卷积网络：池化层，BN与Dropout.mp4 |
2023-12-31 05:45:20 |
467.97MB |
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58.Lesson-16.6.1-复现经典架构(1)：LeNet5.mp4 |
2023-12-31 05:45:20 |
374.24MB |
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59.Lesson-16.6.2-复现经典架构(2)：AlexNet.mp4 |
2023-12-31 05:45:20 |
770.77MB |
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60.Lesson-16.7-如何拓展网络深度：VGG架构.mp4 |
2023-12-31 05:45:20 |
213.36MB |
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61.Lesson-16.8.1-感受野（上）：定义与性质.mp4 |
2023-12-31 05:45:20 |
616.97MB |
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62.Lesson-16.8.2-感受野（下）：膨胀卷积，计算感受野大小.mp4 |
2023-12-31 05:45:20 |
675.63MB |
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63.Lesson-16.9-平移不变性.mp4 |
2023-12-31 05:45:20 |
528.63MB |
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64.Lesson-16.10-卷积层的参数量计算，1x1卷积核.mp4 |
2023-12-31 05:45:20 |
402.89MB |
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65.Lesson-16.11-分组卷积与深度可分离卷积.mp4 |
2023-12-31 05:45:20 |
392.89MB |
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66.Lesson-16.12-全连接层的参数，用nn.Sequential复现VGG16.mp4 |
2023-12-31 05:45:20 |
648.87MB |
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67.Lesson-16.13-全局平均池化，NiN网络的复现.mp4 |
2023-12-31 05:45:20 |
545.96MB |
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68.Lesson-16.14-GoogLeNet：思想与具体架构.mp4 |
2023-12-31 05:45:20 |
739.65MB |
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69.Lesson-16.15-GoogLeNet的复现.mp4 |
2023-12-31 05:45:20 |
1,008.56MB |
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70.Lesson-16.16-残差网络：思想与具体架构.mp4 |
2023-12-31 05:45:20 |
553.92MB |
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71.Lesson-16.17.1-ResNet的复现-(1)-：架构中的陷阱.mp4 |
2023-12-31 05:45:20 |
461.80MB |
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72.Lesson-16.17.2-ResNet的复现-(2)-：卷积块、残差块、瓶颈架构.mp4 |
2023-12-31 05:45:20 |
1.00GB |
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73.Lesson-16.17.3-ResNet的复现-(3)：完整的残差网络.mp4 |
2023-12-31 05:45:20 |
1.08GB |
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74.Lesson-17.1-计算机视觉中的三种基本任务.mp4 |
2023-12-31 05:45:20 |
401.44MB |
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75.Lesson-17.2.1-经典数据集(1)：入门数据集，新手读数据踩坑指南.mp4 |
2023-12-31 05:45:20 |
511.58MB |
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76.Lesson-17.2.2-经典数据集(2)：竞赛数据与其他常用数据.mp4 |
2023-12-31 05:45:20 |
510.58MB |
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77.Lesson-17.3.1-使用自己的图像创造数据集.mp4 |
2023-12-31 05:45:20 |
987.00MB |
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78.Lesson-17.3.2-将二维表及其他结构转化为四维tensor.mp4 |
2023-12-31 05:45:20 |
596.80MB |
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79.Lesson-17.4-图像数据的数据预处理.mp4 |
2023-12-31 05:45:20 |
385.51MB |
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80.Lesson-17.5-数据增强.mp4 |
2023-12-31 05:45:20 |
447.23MB |
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81.Lesson-17.6.1-更强大的优化算法-(1)-AdaGrad.mp4 |
2023-12-31 05:45:20 |
603.25MB |
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82.Lesson-17.6.2-更强大的优化算法(2)-RMSprop与Adam.mp4 |
2023-12-31 05:45:20 |
638.83MB |
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83.Lesson-17.7-调用经典架构.mp4 |
2023-12-31 05:45:20 |
244.63MB |
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84.Lesson-17.8.1-基于ResNet与VGG16自建架构.mp4 |
2023-12-31 05:45:20 |
391.32MB |
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85.Lesson-17.8.2-基于普通卷积层和池化层自建架构.mp4 |
2023-12-31 05:45:20 |
285.16MB |
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86.Lesson-17.9-有监督算法的预训练.迁移学习.mp4 |
2023-12-31 05:45:20 |
688.17MB |
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87.Lesson-17.10-深度学习中的模型选择.mp4 |
2023-12-31 05:45:20 |
430.00MB |
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88.Lesson-17.11(1)-案例1：项目背景.完整流程概述.mp4 |
2023-12-31 05:45:20 |
350.17MB |
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89.Lesson-17.11(2)-案例1：数据与架构.mp4 |
2023-12-31 05:45:20 |
668.51MB |
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90.Lesson-17.11(3)-案例1：提前停止.mp4 |
2023-12-31 05:45:20 |
423.70MB |
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91.Lesson-17.11(4)-案例1：一个完整的训练函数.mp4 |
2023-12-31 05:45:20 |
891.10MB |
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92.Lesson-17.11(5)-准备训练函数所需的全部参数.mp4 |
2023-12-31 05:45:20 |
166.22MB |
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93.Lesson-17.11(6)-GPU内存管理机制、训练函数的GPU版本.mp4 |
2023-12-31 05:45:20 |
309.19MB |
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94.Lesson-17.11(7)-初步训练：模型选择.mp4 |
2023-12-31 05:45:20 |
256.70MB |
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95.Lesson-17.11(8)-模型调优(1)：增加迭代次数，让迭代更稳定.mp4 |
2023-12-31 05:45:20 |
284.81MB |
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96.Lesson-17.11(9)-模型调优(2)：对抗过拟合，其他可探索的方向.mp4 |
2023-12-31 05:45:20 |
255.09MB |
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97.Lesson-18.1-案例背景与benchmark建立.mp4 |
2023-12-31 05:45:20 |
179.93MB |
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98.Lesson-18.2.1-使用OpenCV批量分片高像素图像（上）.mp4 |
2023-12-31 05:45:20 |
195.02MB |
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99.Lesson-18.2.2-使用OpenCV批量分片高像素图像（下）.mp4 |
2023-12-31 05:45:20 |
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