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)
  • 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 and
  • 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)
  • 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)
      • 第一周 卷积神经网络(Foundations of Convolutional Neural Networks)
      • 第二周 深度卷积网络:实例探究(Deep convolutional models: case studies)
      • 第三周 目标检测(Object detection)
        • 3.1 目标定位(Object localization)
        • 3.2 特征点检测(Landmark detection)
        • 3.3 目标检测(Object detection)
        • 3.4 卷积的滑动窗口实现(Convolutional implementation of sliding windows)
        • 3.5 Bounding Box预测(Bounding box predictions)
        • 3.6 交并比(Intersection over union)
        • 3.7 非极大值抑制(Non-max suppression)
        • 3.8 Anchor Boxes
        • 3.9 YOLO 算法(Putting it together: YOLO algorithm)
        • 3.10 候选区域(选修)(Region proposals (Optional))
        • Autonomous driving application - Car detection
        • yolo_utils.py
      • 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)
  • 第五门课 序列模型(Sequence Models)
    • 第五门课 序列模型(Sequence Models)
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  1. 第四门课 卷积神经网络(Convolutional Neural Networks)
  2. 第四门课 卷积神经网络(Convolutional Neural Networks)

第三周 目标检测(Object detection)

3.1 目标定位(Object localization)3.2 特征点检测(Landmark detection)3.3 目标检测(Object detection)3.4 卷积的滑动窗口实现(Convolutional implementation of sliding windows)3.5 Bounding Box预测(Bounding box predictions)3.6 交并比(Intersection over union)3.7 非极大值抑制(Non-max suppression)3.8 Anchor Boxes3.9 YOLO 算法(Putting it together: YOLO algorithm)3.10 候选区域(选修)(Region proposals (Optional))Autonomous driving application - Car detectionyolo_utils.py
Previouskt_utils.pyNext3.1 目标定位(Object localization)

Last updated 6 years ago

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