第19月第8天 斯坦福大学公开课机器学习 (吴恩达 Andrew Ng)
1.斯坦福大学公开课机器学习 (吴恩达 Andrew Ng)
http://open.163.com/special/opencourse/machinelearning.html
笔记
http://cs229.stanford.edu/syllabus.html
http://www.cnblogs.com/jerrylead/default.html?page=3
http://www.cnblogs.com/madrabbit/
https://blog.csdn.net/xiahouzuoxin
https://blog.csdn.net/u010249583
https://blog.csdn.net/stdcoutzyx/article/details/17741475
https://blog.csdn.net/stdcoutzyx/article/details/53869661
https://blog.csdn.net/app_12062011/article/details/50577717
https://blog.csdn.net/antkillerfarm/article/details/52980075
https://www.cnblogs.com/llhthinker/p/5351201.html
https://blog.csdn.net/dingchenxixi/article/details/51479003
随着模型的复杂度增加,虽然偏差会不断减小,但方差先减小后增大,模型的泛化误差也是先减小后增大,因此需要在“欠拟合”和“过拟合”之间寻找合适的模型复杂度。衡量模型的复杂度通常有AIC准则(AkalikeInformation Criterion)、BIC准则(BayesianInformation Criterion)等方法。
https://blog.csdn.net/baidu_35231778/article/details/52221400
2.数学之美
http://mindhacks.cn/2008/09/21/the-magical-bayesian-method/
3.svm
http://www.cnblogs.com/LeftNotEasy/archive/2011/05/02/basic-of-svm.html
http://www.10tiao.com/html/520/201711/2650725003/3.html
https://blog.csdn.net/v_july_v/article/details/7624837
https://blog.csdn.net/lch614730/article/details/17067027
https://www.jianshu.com/u/511ba5d71aef
http://www.cnblogs.com/vipyoumay/p/7560061.html
4.k-means
http://blog.pluskid.org/?p=17
http://www.cnblogs.com/jerrylead/archive/2011/04/06/2006910.html
5.em
https://zhuanlan.zhihu.com/p/24655368
https://blog.csdn.net/zhihua_oba/article/details/73776553
https://www.cnblogs.com/fxjwind/p/3896113.html
https://www.cnblogs.com/xuesong/p/4179459.html
https://www.cnblogs.com/yymn/p/4769736.html
6.pca
https://blog.csdn.net/mmc2015/article/details/42459753
https://blog.csdn.net/chlele0105/article/details/13004499
https://blog.csdn.net/zhangdadadawei/article/details/50929574
https://blog.csdn.net/mmc2015/article/details/42459753
7.svd
https://blog.csdn.net/zhongkejingwang/article/details/43083603
https://blog.csdn.net/xmu_jupiter
http://www.infoq.com/cn/articles/matrix-decomposition-of-recommend-system
https://blog.csdn.net/syani/article/details/52297093
https://blog.csdn.net/american199062/article/details/51344067
8.PRML
https://baijiahao.baidu.com/s?id=1585194960281334902&wfr=spider&for=pc
https://github.com//ctgk/PRML
9.
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机器学习&深度学习视频资料汇总
https://www.cnblogs.com/baihuaxiu/p/6725223.html
- BAT机器学习面试1000题系列 每日刷
- 从最大似然到EM算法浅解 2018.3.7
- 机器学习中的PR曲线和ROC曲线 2018.3.23
- VotingClassifier 模型聚合——投票 2018.3.25
- 非平衡数据机器学习 2018.3.25
- 机器学习:概率校准 2018.3.25
- 机器学习中的损失函数 (着重比较:hinge loss vs softmax loss 2018.3.25
- 机器学习常用的分类器比较 2018.3.25
- 线性判别分析(Linear Discriminant Analysis) 2018.3.26
- 机器学习常用 35 大算法盘点(附思维导图) 2018.3.26
- 机器学习的分类与主要算法对比 2018.3.26
- logistic函数和softmax函数 2018.3.27
- Logistic Regression(逻辑回归)原理及公式推导 2018.3.27
- 机器学习算法—随机森林实现(包括回归和分类) 2018.4.7
http://ykksmile.top/posts/55073/
这位成功转型机器学习的老炮,想把他多年的经验分享给你
https://blog.csdn.net/wemedia/details.html?id=38193
https://www.jianshu.com/u/12201cdd5d7a
code
https://github.com/lzhe72/MachineLearning/