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file:github地址.txt file:Mastering Feature Engineering Principles and Techniques for Data Scientists (Early Release)-O’reilly (2016).pdf file:ISLR Seventh Printing.pdf file:632-Redis中取出数据保存到Mongo.mp4 file:631-分布式3写法.mp4 file:628-scrapy-redis环境搭建-windows软件安装.mp4 file:625scrapy-redis的介绍.mp4 file:624-某家网站数据的保存.mp4 file:622-selenium与scrapy的结合.mp4 file:zfdev_tree.txt file:7-9 人脸识别与特征脸.mp4 file:7-8 使用PCA对数据进行降噪.mp4 file:7-7 试手MNIST数据集.mp4 file:7-6 scikit-learn中的PCA.mp4 file:7-5 高维数据映射为低维数据.mp4 file:7-4 求数据的前n个主成分.mp4 file:7-2 使用梯度上升法求解PCA问题.mp4 file:7-1 什么是PCA.mp4 file:6-9 有关梯度下降法的更多深入讨论.mp4 file:6-8 如何确定梯度计算的准确性 调试梯度下降法.mp4 file:6-5 梯度下降的向量化和数据标准化.mp4 file:6-4 实现线性回归中的梯度下降法.mp4 file:5-8 实现多元线性回归.mp4 file:5-6 最好的衡量线性回归法的指标 R Squared.mp4 file:5-5 衡量线性回归法的指标 MSE,RMS,MAE.mp4 file:5-4 向量化.mp4 file:5-10 线性回归的可解性和更多思考.mp4
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