51CTO-深度学习框架Tensorflow实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 51CTO-深度学习框架Tensorflow实战https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 22.Alexnet网络
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 10.LSTM文本分类任务实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 20.卷积神经网络实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 18.Tensorflow初识
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 03.回归任务
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 21.递归神经网络模型
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 06.图像数据增强
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 05.识别实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 11.CNN网络实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 08.递归神经网络与词向量
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 12.时间序列预测
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 07.迁移学习实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 23.Tensorboard可视化模块
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 16.CycleGan实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 27.Tensorflow项目实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 01.tensorflow环境安装
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 24.tfrecord数据源制作
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 09.词向量模型
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 19.Tensorflow神经网络
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 17.Resnet实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 13.框架BERT
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 资料+代码
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 25. CNN文本分类
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 04.卷积神经
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 15.对抗生成网络实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 02.神经网络
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 14.BERT实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 26.Resnet残差网络
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 22-2 数据读取.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 22-1 环境配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 22-4 加载训练好参数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 22-3 网络结构定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20-4 网络迭代训练.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20-3 网络架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20-2 数据读取.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20-5 测试效果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20-1 卷积神经网络实战：猫狗识别.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-2 Tensorflow中的变量.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-4 实现线性回归算法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-5 Mnist数据集简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-3 变量常用操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-6 逻辑回归算法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18-1 Tensorflow简介与安装.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-5 数据生成器构造.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-4 embedding层向量制作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-3 数据映射表制作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-8 训练策略指定.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-9 训练文本分类模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-2 RNN模型输入数据维度解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-1 任务目标与数据介绍.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-7 自定义网络模型架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10-6 双向RNN模型定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 5-3 网络架构配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 5-4 卷积模型训练与识别效果展示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 5-1 猫狗识别任务与数据简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 5-2 卷积网络涉及参数解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 21-4 RNN预测时间序列.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 21-1 RNN网络基本架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 21-3 RNN实现自己的小demo.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 21-2 实现RNN网络架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-5 分类模型构建.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-1 任务目标与数据集简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-7 模型保存与读取实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-4 模型超参数调节与预测结果展示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-3 网络模型训练.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-2 建模流程与API文档.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 3-6 tf.data模块解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 6-2 图像数据变换.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 6-1 数据增强概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 6-3 猫狗识别任务数据增强实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12-4 多特征预测结果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12-2 构建时间序列数据.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12-5 序列结果预测.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12-1 任务目标与数据源.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12-3 训练时间序列数据预测结果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-4 训练数据构建.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-1 RNN网络架构解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-2 词向量模型通俗解释.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-6 负采样方案.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-5 CBOW与Skip-gram模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 8-3 模型整体框架.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-3 Resnet原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-5 Callback模块与迁移学习实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-2 迁移学习策略.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-1 迁移学习的目标.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-7 图像数据处理实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-4 加载训练好的经典网络模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 7-6 tfrecords数据源制作方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 11-1 CNN应用于文本任务原理解析.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 11-2 整体流程解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 11-3 网络架构设计与训练.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 27-3 Tensorflow案例实战视频课程21 卷积网络模型定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 27-4 Tensorflow案例实战视频课程22 迭代及测试网络效果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 27-1 Tensorflow案例实战视频课程19 验证码数据生成.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 27-2 Tensorflow案例实战视频课程20 构造网络的输入数据和标签.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 1-2 Tensorflow2版本简介与心得.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 1-4 tf基础操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 1-1 课程简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 1-3 Tensorflow2版本安装方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 23-1 Tensorboard可视化展示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 23-2 展示效果.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 23-4 参数对结果的影响.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 23-3 统计可视化展示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-12 生成与判别损失函数指定.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-13 额外补充：VISDOM可视化配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-1 CycleGan网络所需数据.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-9 生成网络模块构造.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-3 PatchGan判别网络原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-8 数据读取与预处理操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-7 Cycle开源项目简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-11 损失函数：identity loss计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-5 生成与判别器损失函数定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-4 数据与环境配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-6 整体损失模块解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-10 判别网络模块构造.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16-2 CycleGan整体网络架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 24-4 加载tfrecord进行分类任务.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 24-1 生成自己的数据集.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 24-3 生成数据源.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 24-2 读取数据.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 9-4 训练batch数据制作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 9-1 任务流程解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 9-5 损失函数定义与训练结果展示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 9-3 文本词预处理操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 9-2 模型定义参数设置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 19-2 卷积网络结构基本定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 19-1 神经网络结构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 19-3 卷积神经网络迭代.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 19-4 Cifar-10图像分类任务.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-7 前向传播配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-1 额外补充-Resnet论文解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-8 训练resnet模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-3 项目结构概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-6 网络架构层次解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-2 额外补充-Resnet网络架构解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-4 数据集处理方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17-5 训练数据构建.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 资料+代码.exe
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 25-4 完成预测分类任务.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 25-3 卷积网络定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 25-2 文本分类任务特征定义.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 25-1 CNN文本分类任务概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-3 注意力机制的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-1 BERT任务目标概述.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-4 self-attention计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-5 特征分配与softmax机制.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-7 位置编码与多层堆叠.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-8 transformer整体架构梳理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-9 BERT模型训练方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-10 训练实例.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-6 Multi-head的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13-2 传统解决方案遇到的问题.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-5 步长与卷积核大小对结果的影响.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-7 特征图尺寸计算与参数共享.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-8 池化层的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-4 得到特征图表示.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-12 感受野的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-3 卷积特征值计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-10 VGG网络架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-6 边缘填充方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-2 卷积的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-1 卷积网络应用领域.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-9 整体网络架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 4-11 残差网络Resnet.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 26-3 残差网络细节.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 26-1 Resnet网络原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 26-2 网络流程设计.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-9 mask机制.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-8 加入位置编码特征.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-4 数据预处理模块.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-11 完成Transformer模块构建.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-7 加入额外编码特征.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-6 Embedding层的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-1 BERT开源项目简介.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-2 项目参数配置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-3 数据读取模块.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-5 tfrecord制作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-10 构建QKV矩阵.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14-12 训练BERT模型.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-12 正则化与激活函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-6 损失函数的作用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-1 深度学习要解决的问题.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-7 前向传播整体流程.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-2 深度学习应用领域.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-9 神经网络整体架构.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-11 神经元个数对结果的影响.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-3 计算机视觉任务.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-4 视觉任务中遇到的问题.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-10 神经网络架构细节.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-8 返向传播计算方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-13 神经网络过拟合解决方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 2-5 得分函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15-3 DCGAN网络架构与流程解读.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15-1 对抗生成网络通俗解释.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15-2 GAN网络组成.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15-4 网络架构设计.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15-5 损失函数定义与训练.mp4
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