# ML-notes
**Repository Path**: dinglide/ML-notes
## Basic Information
- **Project Name**: ML-notes
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 2
- **Created**: 2020-12-31
- **Last Updated**: 2020-12-31
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# ML-notes
[](https://github.com/Sakura-gh/ML-notes/issues) [](https://github.com/Sakura-gh/ML-notes/network) [](https://github.com/Sakura-gh/ML-notes/stargazers) [](https://github.com/Sakura-gh/ML-notes/blob/master/LICENSE)
notes about machine learning
很喜欢一句话：**应用之道，存乎一心**，与大家共勉
ps：如果我的笔记对你有帮助，给个star叭！查看机器学习笔记的PDF订阅版(**301页**)以及更多计算机相关笔记，欢迎大家关注微信公众号"Sakura的知识库"~
##### ML-Assignments
ML配套Assignments (ppt+code)：https://github.com/Sakura-gh/ML-assignments
内容包括：Regression, Classification, CNN, RNN, Explainable AI, Adversarial Attack, Network Compression, Seq2Seq, GAN, Transfer Learning, Meta Learning, Life-long Learning, Reforcement Learning.
##### pages
the github page is: https://Sakura-gh.github.io/ML-notes
you can also visit gitee page for quicker Internet in China: https://Sakura-gh.gitee.io/ml-notes
##### html链接：
[1_Introduction]( https://sakura-gh.github.io/ML-notes/ML-notes-html/1_Introduction.html)
[2_Regression Case Study]( https://sakura-gh.github.io/ML-notes/ML-notes-html/2_Regression-Case-Study.html)
[3_Regression demo(Adagrad)]( https://sakura-gh.github.io/ML-notes/ML-notes-html/3_Regression-demo(Adagrad).html)
[4_Where does the error come from](https://sakura-gh.github.io/ML-notes/ML-notes-html/4_Where-does-the-error-come-from.html)
[5_Gradient Descent](https://sakura-gh.github.io/ML-notes/ML-notes-html/5_Gradient-Descent.html)
[6_Classification](https://sakura-gh.github.io/ML-notes/ML-notes-html/6_Classification.html)
[7_Logistic Regression](https://sakura-gh.github.io/ML-notes/ML-notes-html/7_Logistic-Regression.html)
[8_Deep Learning](https://sakura-gh.github.io/ML-notes/ML-notes-html/8_Deep-Learning.html)
[9_Backpropagation](https://sakura-gh.github.io/ML-notes/ML-notes-html/9_Backpropagation.html)
[10_Keras](https://sakura-gh.github.io/ML-notes/ML-notes-html/10_Keras.html)
[11_Convolutional Neural Network part1](https://sakura-gh.github.io/ML-notes/ML-notes-html/11_Convolutional-Neural-Network-part1.html)
[12_Convolutional Neural Network part2](https://sakura-gh.github.io/ML-notes/ML-notes-html/12_Convolutional-Neural-Network-part2.html)
[13_Tips for Deep Learning](https://sakura-gh.github.io/ML-notes/ML-notes-html/13_Tips-for-Deep-Learning.html)
[14_Why Deep](https://sakura-gh.github.io/ML-notes/ML-notes-html/14_Why-Deep.html)
[15_Semi-supervised Learning](https://sakura-gh.github.io/ML-notes/ML-notes-html/15_Semi-supervised-Learning.html)
[16_Unsupervised Learning Introduction](https://sakura-gh.github.io/ML-notes/ML-notes-html/16_Unsupervised-Learning-Introduction.html)
[17_Unsupervised Learning PCA part1](https://sakura-gh.github.io/ML-notes/ML-notes-html/17_Unsupervised-Learning-PCA-part1.html)
[18_Unsupervised Learning PCA part2](https://sakura-gh.github.io/ML-notes/ML-notes-html/18_Unsupervised-Learning-PCA-part2.html)
[19_Matrix Factorization](https://sakura-gh.github.io/ML-notes/ML-notes-html/19_Matrix-Factorization.html)
[20_Unsupervised Learning Word Embedding](https://sakura-gh.github.io/ML-notes/ML-notes-html/20_Unsupervised-Learning-Word-Embedding.html)
[21_Unsupervised Learning Neighbor Embedding](https://sakura-gh.github.io/ML-notes/ML-notes-html/21_Unsupervised-Learning-Neighbor-Embedding.html)
[22_Unsupervised Learning Deep Auto-encoder](https://sakura-gh.github.io/ML-notes/ML-notes-html/22_Unsupervised-Learning-Deep-Auto-encoder.html)
[23_Unsupervised Learning Generation](https://sakura-gh.github.io/ML-notes/ML-notes-html/23_Unsupervised-Generation.html)
[24_Transfer Learning](https://sakura-gh.github.io/ML-notes/ML-notes-html/24_Transfer-Learning.html)
[25_Support Vector Machine](https://sakura-gh.github.io/ML-notes/ML-notes-html/25_Support-Vector-Machine.html)
[26_Recurrent Neural Network part1](https://sakura-gh.github.io/ML-notes/ML-notes-html/26_Recurrent-Neural-Network-part1.html)
[27_Recurrent Neural Network part2](https://sakura-gh.github.io/ML-notes/ML-notes-html/27_Recurrent-Neural-Network-part2.html)
##### csdn博客链接：
[机器学习系列1-机器学习概念及介绍](https://blog.csdn.net/weixin_44406200/article/details/104060561)
[机器学习系列2-回归案例研究](https://blog.csdn.net/weixin_44406200/article/details/104071036)
[梯度下降代码举例：Gradient Descent Demo(Adagrad)](https://blog.csdn.net/weixin_44406200/article/details/104075986)
[机器学习系列4-模型的误差来源及减少误差的方法](https://blog.csdn.net/weixin_44406200/article/details/104088554)
[机器学习系列5-梯度下降法](https://blog.csdn.net/weixin_44406200/article/details/104256006)
[机器学习系列6-分类问题(概率生成模型)](https://blog.csdn.net/weixin_44406200/article/details/104272160)
[机器学习系列7-逻辑回归](https://blog.csdn.net/weixin_44406200/article/details/104288916)
[机器学习系列8-深度学习简介](https://blog.csdn.net/weixin_44406200/article/details/104299958)
[机器学习系列9-反向传播](https://blog.csdn.net/weixin_44406200/article/details/104310991)
[机器学习系列10-手写数字识别(Keras2.0)](https://blog.csdn.net/weixin_44406200/article/details/104328947)
[机器学习系列11-卷积神经网络CNN part1](https://blog.csdn.net/weixin_44406200/article/details/104370738)
[机器学习系列12-卷积神经网络CNN part2](https://blog.csdn.net/weixin_44406200/article/details/104392592)
[机器学习系列13-深度学习的技巧和优化方法](https://blog.csdn.net/weixin_44406200/article/details/104430737)
[机器学习系列14-为什么要做“深度”学习](https://blog.csdn.net/weixin_44406200/article/details/104452873)
[机器学习系列15-半监督学习](https://blog.csdn.net/weixin_44406200/article/details/106991717)
[机器学习系列16-无监督学习引言](https://blog.csdn.net/weixin_44406200/article/details/107029531)
[机器学习系列17-无监督学习之PCA推导(Ⅰ)](https://blog.csdn.net/weixin_44406200/article/details/107082637)
[机器学习系列18-无监督学习之PCA深入探讨(Ⅱ)](https://blog.csdn.net/weixin_44406200/article/details/107082680)
[机器学习系列19-矩阵分解&推荐系统初步](https://blog.csdn.net/weixin_44406200/article/details/107099894)
[机器学习系列20-无监督学习之词嵌入](https://blog.csdn.net/weixin_44406200/article/details/107168089)
[机器学习系列21-无监督学习之近邻嵌入](https://blog.csdn.net/weixin_44406200/article/details/107305230)
[机器学习系列22-无监督学习之自编码器](https://blog.csdn.net/weixin_44406200/article/details/107305267)
[机器学习系列23-无监督学习之生成模型](https://blog.csdn.net/weixin_44406200/article/details/107305305)
[机器学习系列24-迁移学习](https://blog.csdn.net/weixin_44406200/article/details/107305326)
[机器学习系列25-支持向量机](https://blog.csdn.net/weixin_44406200/article/details/107693177)
[机器学习系列26-循环神经网络RNN(Ⅰ)](https://blog.csdn.net/weixin_44406200/article/details/107693331)
[机器学习系列27-循环神经网络RNN(Ⅱ)](https://blog.csdn.net/weixin_44406200/article/details/107812374)
##### 代码链接：
[Gradient Descent Demo(Adagrad)]( https://sakura-gh.github.io/ML-notes/code/Gradient-Descent-Demo/Gradient-Descent-Demo.html)
[手写数字识别(Keras2.0)](https://github.com/Sakura-gh/ML-notes/blob/master/code/Digits-Detection/digits-detection.py)
[手写数字识别CNN实现(Keras2.0)](https://github.com/Sakura-gh/ML-notes/blob/master/code/Digits-Detection/digits-detection-cnn.py)
[Keras实战小经验](https://github.com/Sakura-gh/ML-notes/blob/master/keras-tips.md)
[PyTorch简易入门](https://github.com/Sakura-gh/ML-notes/blob/master/code/pytorch)
##### LICENSE：
GPL-2.0
##### 温馨提示：
图片加载可能会有些许缓慢，请耐心等待\\(\^o\^)/
##### 赞赏作者：
如果读后有收获，请作者喝杯咖啡吧，您的支持就是我最大的更新动力~
##### PDF订阅版：
关注公众号“Sakura的知识库”可订阅：
##### ML GPU：
