├── .gitignore ├── .ipynb_checkpoints ├── Untitled1-checkpoint.ipynb ├── keras_demo-checkpoint.ipynb └── 感知器-checkpoint.ipynb ├── README.md ├── 前馈神经网络 ├── .ipynb_checkpoints │ ├── 使用正则化技术提高泛化能力-checkpoint.ipynb │ ├── 关于隐层和隐层神经元的实验-checkpoint.ipynb │ ├── 实现一个单层神经网络-checkpoint.ipynb │ ├── 实现一个自动编码器-checkpoint.ipynb │ ├── 实现感知器-checkpoint.ipynb │ ├── 开始使用激活函数-checkpoint.ipynb │ ├── 构建一个多层神经网络-checkpoint.ipynb │ ├── 测试不同的优化器-checkpoint.ipynb │ ├── 添加Dropout以防止过拟合-checkpoint.ipynb │ └── 调整损失函数-checkpoint.ipynb ├── README.md ├── checkpoints │ └── multi_layer_best_model.h5 ├── data │ ├── .ipynb_checkpoints │ │ └── Untitled-checkpoint.ipynb │ ├── Bike-Sharing-Dataset.zip │ ├── Bike-Sharing-Dataset │ │ ├── Readme.txt │ │ ├── day.csv │ │ └── hour.csv │ ├── best_model_adadelta.h5 │ ├── best_model_adam.h5 │ ├── best_model_adamax.h5 │ ├── best_model_nadam.h5 │ ├── best_model_rmsprop-0001.h5 │ ├── best_model_rmsprop.h5 │ ├── best_model_sgd-0001.h5 │ ├── best_model_sgd.h5 │ ├── mnist.npz │ └── winequality-red.csv ├── 使用正则化技术提高泛化能力.ipynb ├── 关于隐层和隐层神经元的实验.ipynb ├── 实现一个单层神经网络.ipynb ├── 实现一个自动编码器.ipynb ├── 实现感知器.ipynb ├── 开始使用激活函数.ipynb ├── 构建一个多层神经网络.ipynb ├── 测试不同的优化器.ipynb ├── 添加Dropout以防止过拟合.ipynb └── 调整损失函数.ipynb ├── 卷积神经网络 ├── .ipynb_checkpoints │ ├── 使用批量标准化进行优化-checkpoint.ipynb │ ├── 使用滤波器和参数共享-checkpoint.ipynb │ ├── 实现卷积自动编码器-checkpoint.ipynb │ ├── 实验不同类型的初始化-checkpoint.ipynb │ ├── 将一维CNN应用于文本-checkpoint.ipynb │ ├── 应用层合并技术-checkpoint.ipynb │ └── 理解填充和步长-checkpoint.ipynb ├── README.md ├── data │ ├── .ipynb_checkpoints │ │ └── 使用批量标准化进行优化-checkpoint.ipynb │ ├── mnist.npz │ └── 使用批量标准化进行优化.ipynb ├── 使用批量标准化进行优化.ipynb ├── 使用滤波器和参数共享.ipynb ├── 实现卷积自动编码器.ipynb ├── 实验不同类型的初始化.ipynb ├── 将一维CNN应用于文本.ipynb ├── 应用层合并技术.ipynb └── 理解填充和步长.ipynb ├── 强化学习 ├── .ipynb_checkpoints │ ├── 实现深度Ｑ学习算法-checkpoint.ipynb │ └── 实现策略梯度-checkpoint.ipynb ├── README.md ├── 实现深度Ｑ学习算法.ipynb └── 实现策略梯度.ipynb ├── 循环神经网络 ├── .ipynb_checkpoints │ ├── Chapter 4 - Adding attention-checkpoint.ipynb │ ├── 使用GRU-checkpoint.ipynb │ ├── 字符级文本生成-checkpoint.ipynb │ ├── 实现一个简单的RNN-checkpoint.ipynb │ ├── 实现双向RNN-checkpoint.ipynb │ ├── 添加Attention-checkpoint.ipynb │ └── 添加LSTM-checkpoint.ipynb ├── README.md ├── 使用GRU.ipynb ├── 字符级文本生成.ipynb ├── 实现一个简单的RNN.ipynb ├── 实现双向RNN.ipynb ├── 添加Attention.ipynb └── 添加LSTM.ipynb ├── 时间序列和结构化数据 ├── .ipynb_checkpoints │ ├── 使用浅层神经网络进行二元分类-checkpoint.ipynb │ ├── 使用神经网络预测股票价格-checkpoint.ipynb │ └── 预测共享单车需求-checkpoint.ipynb ├── README.md ├── 使用浅层神经网络进行二元分类.ipynb ├── 使用神经网络预测股票价格.ipynb └── 预测共享单车需求.ipynb ├── 框架 └── keras_demo.ipynb ├── 游戏智能体和机器人 ├── .ipynb_checkpoints │ ├── 用GA优化超参数-checkpoint.ipynb │ ├── 通过深度强化学习来玩游戏-checkpoint.ipynb │ └── 通过端到端学习来驾驶汽车-checkpoint.ipynb ├── README.md ├── 用GA优化超参数.ipynb ├── 通过深度强化学习来玩游戏.ipynb └── 通过端到端学习来驾驶汽车.ipynb ├── 生成对抗网络 ├── .ipynb_checkpoints │ ├── 了解GAN-checkpoint.ipynb │ ├── 使用SRGAN-checkpoint.ipynb │ └── 实现DCGAN-checkpoint.ipynb ├── README.md ├── 了解GAN.ipynb ├── 使用SRGAN.ipynb └── 实现DCGAN.ipynb ├── 网络内部构造 ├── .ipynb_checkpoints │ ├── 冻结层-checkpoint.ipynb │ ├── 分析网络权重-checkpoint.ipynb │ ├── 存储网络结构并训练权重-checkpoint.ipynb │ └── 用TensorBoard可视化训练过程-checkpoint.ipynb ├── README.md ├── 冻结层.ipynb ├── 分析网络权重.ipynb ├── 存储网络结构并训练权重.ipynb └── 用TensorBoard可视化训练过程.ipynb ├── 自然语言处理 ├── .ipynb_checkpoints │ ├── 句子翻译-checkpoint.ipynb │ ├── 情绪分析-checkpoint.ipynb │ └── 文本摘要-checkpoint.ipynb ├── README.md ├── 句子翻译.ipynb ├── 情绪分析.ipynb └── 文本摘要.ipynb ├── 计算机视觉 ├── .ipynb_checkpoints │ ├── 人脸识别-checkpoint.ipynb │ ├── 图像中的目标分类-checkpoint.ipynb │ ├── 增广图像-checkpoint.ipynb │ ├── 寻找人脸面部关键点-checkpoint.ipynb │ ├── 将风格转化为图像-checkpoint.ipynb │ ├── 用U-net将图像分类-checkpoint.ipynb │ ├── 目标在图像中的本地化-checkpoint.ipynb │ └── 语义分割与场景理解-checkpoint.ipynb ├── README.md ├── 人脸识别.ipynb ├── 图像中的目标分类.ipynb ├── 增广图像.ipynb ├── 寻找人脸面部关键点.ipynb ├── 将风格转化为图像.ipynb ├── 用U-net将图像分类.ipynb ├── 目标在图像中的本地化.ipynb └── 语义分割与场景理解.ipynb ├── 语音识别和视频分析 ├── .ipynb_checkpoints │ ├── 从零开始实现语音识别流程-checkpoint.ipynb │ ├── 使用深度学习理解视频-checkpoint.ipynb │ └── 使用语音识别技术辨别讲话人-checkpoint.ipynb ├── README.md ├── 从零开始实现语音识别流程.ipynb ├── 使用深度学习理解视频.ipynb └── 使用语音识别技术辨别讲话人.ipynb ├── 超参数选择、调优和神经网络学习 ├── .ipynb_checkpoints │ ├── TensorBoard可视化训练过程-checkpoint.ipynb │ ├── 使用批量生成器-checkpoint.ipynb │ ├── 学习率及其调度-checkpoint.ipynb │ ├── 数据增广使模型更加鲁棒-checkpoint.ipynb │ ├── 比较不同的优化器-checkpoint.ipynb │ ├── 添加Dropout以防止过拟合-checkpoint.ipynb │ └── 网格搜索调参-checkpoint.ipynb ├── README.md ├── TensorBoard可视化训练过程.ipynb ├── 使用批量生成器.ipynb ├── 学习率及其调度.ipynb ├── 数据增广使模型更加鲁棒.ipynb ├── 比较不同的优化器.ipynb ├── 添加Dropout以防止过拟合.ipynb └── 网格搜索调参.ipynb └── 预训练模型 ├── .ipynb_checkpoints ├── 使用GoogleLeNet Inception进行大规模视觉识别-checkpoint.ipynb ├── 对新类别使用预训练的VGG模型-checkpoint.ipynb ├── 用ResNet提取瓶颈特征-checkpoint.ipynb └── 用Xception细调-checkpoint.ipynb ├── README.md ├── 使用GoogleLeNet Inception进行大规模视觉识别.ipynb ├── 对新类别使用预训练的VGG模型.ipynb ├── 用ResNet提取瓶颈特征.ipynb └── 用Xception细调.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | *.mat 2 | *.zip 3 | 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