摘要: http://www.ai-start.com/dl2017/html/lesson5-week1.html 序列模型(Sequence Models) 为什么选择序列模型?(Why Sequence Models?) 数学符号(Notation) 这个输入数据是9个单词组成的序列,所以最终我们会有 阅读全文
posted @ 2020-12-01 21:07 Stark0x01 阅读(354) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson4-week4.html 特殊应用:人脸识别和神经风格转换(Special applications: Face recognition &Neural style transfer) 什么是人脸识别?(What i 阅读全文
posted @ 2020-12-01 21:06 Stark0x01 阅读(188) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson4-week3.html 目标检测(Object detection) 目标定位(Object localization) 特征点检测(Landmark detection) 目标检测(Object detectio 阅读全文
posted @ 2020-12-01 21:03 Stark0x01 阅读(175) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson4-week2.html 深度卷积网络:实例探究(Deep convolutional models: case studies) 为什么要进行实例探究?(Why look at case studies?) LeN 阅读全文
posted @ 2020-12-01 21:02 Stark0x01 阅读(173) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson4-week1.html 卷积神经网络(Convolutional Neural Networks) 计算机视觉(Computer vision) 缘检测示例(Edge detection example) 为什么这 阅读全文
posted @ 2020-12-01 21:01 Stark0x01 阅读(105) 评论(0) 推荐(0)
摘要: 机器学习策略(2)(ML Strategy (2)) 进行误差分析(Carrying out error analysis) 清除标注错误的数据(Cleaning up Incorrectly labeled data) 亲自检查数据非常值得 快速搭建你的第一个系统,并进行迭代(Build your 阅读全文
posted @ 2020-12-01 20:59 Stark0x01 阅读(95) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson3-week1.html 机器学习(ML)策略(1)(ML strategy(1)) 为什么是ML策略?(Why ML Strategy?) 正交化(Orthogonalization) 单一数字评估指标(Singl 阅读全文
posted @ 2020-12-01 20:57 Stark0x01 阅读(119) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson2-week3.html 超参数调试、Batch正则化和程序框架(Hyperparameter tuning) 调试处理(Tuning process) 关于训练深度最难的事情之一是你要处理的参数的数量,从学习速率$ 阅读全文
posted @ 2020-12-01 20:55 Stark0x01 阅读(182) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson2-week2.html 优化算法 (Optimization algorithms) Mini-batch 梯度下降(Mini-batch gradient descent) 可以把训练集分割为小一点的子集训练,这 阅读全文
posted @ 2020-12-01 20:52 Stark0x01 阅读(171) 评论(0) 推荐(0)
摘要: http://www.ai-start.com/dl2017/html/lesson2-week1.html 深度学习的实践层面(Practical aspects of Deep Learning) 训练,验证,测试集(Train / Dev / Test sets) 偏差,方差(Bias /Va 阅读全文
posted @ 2020-12-01 20:51 Stark0x01 阅读(147) 评论(0) 推荐(0)