DeepLearning.ai深度学习课程笔记
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  • Introduction
  • 第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)chevron-right
  • 第二门课 改善深层神经网络:超参数调试、 正 则 化 以 及 优 化 (Improving Deep Neural Networks:Hyperparameter tuning, Regulariza
    • 第二门课 改善深层神经网络:超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization andchevron-right
  • 第三门课 结构化机器学习项目(Structuring Machine Learning Projects)
    • 第三门课 结构化机器学习项目(Structuring Machine Learning Projects)chevron-right
  • 第四门课 卷积神经网络(Convolutional Neural Networks)
    • 第四门课 卷积神经网络(Convolutional Neural Networks)chevron-right
      • 第一周 卷积神经网络(Foundations of Convolutional Neural Networks)chevron-right
      • 第二周 深度卷积网络:实例探究(Deep convolutional models: case studies)chevron-right
        • 2.1 经典网络(Classic networks)
        • 2.2 残差网络(Residual Networks (ResNets))
        • 2.3 残差网络为什么有用?(Why ResNets work?)
        • 2.4 网络中的网络以及 1×1 卷积(Network in Network and 1×1 convolutions)
        • 2.5 谷歌 Inception 网络简介(Inception network motivation)
        • 2.6 Inception 网络(Inception network)
        • 2.7 迁移学习(Transfer Learning)
        • 2.8 数据扩充(Data augmentation)
        • 2.9 计算机视觉现状(The state of computer vision)
        • Residual Networks
        • Keras tutorial - the Happy House
        • kt_utils.py
      • 第三周 目标检测(Object detection)chevron-right
      • 第四周 特殊应用:人脸识别和神经风格转换(Special applications: Face recognition &Neural style transfer)chevron-right
  • 第五门课 序列模型(Sequence Models)
    • 第五门课 序列模型(Sequence Models)chevron-right
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  1. 第四门课 卷积神经网络(Convolutional Neural Networks)chevron-right
  2. 第四门课 卷积神经网络(Convolutional Neural Networks)

第二周 深度卷积网络:实例探究(Deep convolutional models: case studies)

2.1 经典网络(Classic networks)chevron-right2.2 残差网络(Residual Networks (ResNets))chevron-right2.3 残差网络为什么有用?(Why ResNets work?)chevron-right2.4 网络中的网络以及 1×1 卷积(Network in Network and 1×1 convolutions)chevron-right2.5 谷歌 Inception 网络简介(Inception network motivation)chevron-right2.6 Inception 网络(Inception network)chevron-right2.7 迁移学习(Transfer Learning)chevron-right2.8 数据扩充(Data augmentation)chevron-right2.9 计算机视觉现状(The state of computer vision)chevron-rightResidual Networkschevron-rightKeras tutorial - the Happy Housechevron-rightkt_utils.pychevron-right
Previouscnn_utilschevron-leftNext2.1 经典网络(Classic networks)chevron-right

Last updated 6 years ago

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