Classic machine learning

这里是我汇集总结的经典机器学习十大算法.

Ps:

在总结的过程中,查询和引用了很多资料,以至于有些来源尚未注明,如果大家发现需要我重新注明来源欢迎发邮件提醒,我将尽快注明.

如果发现其中有问题的,欢迎issue.

其中对于EM计算HMM(Hidden Markove Model)我没有将其归纳,由于现在都是使用CTC居多.所以来日我会在DeepLearn 中详细说明HMM转CTC在NLP方面的应用.

Good Luck ~~~

——— By Joker.

Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
data_set		data_set
picture		picture
.gitignore		.gitignore
1-K nearest neighbor.ipynb		1-K nearest neighbor.ipynb
2-Decision Trees.ipynb		2-Decision Trees.ipynb
3-1Naive Bayes(Theory).ipynb		3-1Naive Bayes(Theory).ipynb
3-2Naive Bayes(Application).ipynb		3-2Naive Bayes(Application).ipynb
3-3 Naive Bayes(Gaussian).ipynb		3-3 Naive Bayes(Gaussian).ipynb
4-1 Logistic Regression(Theory01).ipynb		4-1 Logistic Regression(Theory01).ipynb
4-2 Logistic Regression(Theory02).ipynb		4-2 Logistic Regression(Theory02).ipynb
4-3 Logistic regression(Application).ipynb		4-3 Logistic regression(Application).ipynb
4-4 Softmax(Theory).ipynb		4-4 Softmax(Theory).ipynb
4-5 Softmax(Application).ipynb		4-5 Softmax(Application).ipynb
5-1Support vector machines(Theory).ipynb		5-1Support vector machines(Theory).ipynb
5-2Support vector machines(SMO).ipynb		5-2Support vector machines(SMO).ipynb
5-3 Support vector machines(Application01).ipynb		5-3 Support vector machines(Application01).ipynb
6-AdaBoost.ipynb		6-AdaBoost.ipynb
7-1EM.ipynb		7-1EM.ipynb
7-2Gaussian Mixture(Theory).ipynb		7-2Gaussian Mixture(Theory).ipynb
7-3Gaussian Mixture Model(Application).ipynb		7-3Gaussian Mixture Model(Application).ipynb
8-1 K-Means(Theory).ipynb		8-1 K-Means(Theory).ipynb
8-2 K-Means(Application).ipynb		8-2 K-Means(Application).ipynb
LICENSE		LICENSE
README.md		README.md

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