# ML-notes

**Repository Path**: nonli/ML-notes

## Basic Information

- **Project Name**: ML-notes
- **Description**: notes about machine learning
- **Primary Language**: Python
- **License**: GPL-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 5
- **Created**: 2020-10-30
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ML-notes

[![GitHub issues](https://img.shields.io/github/issues/Sakura-gh/ML-notes?color=ffa07a)](https://github.com/Sakura-gh/ML-notes/issues) [![GitHub forks](https://img.shields.io/github/forks/Sakura-gh/ML-notes?color=20b2aa)](https://github.com/Sakura-gh/ML-notes/network) [![GitHub stars](https://img.shields.io/github/stars/Sakura-gh/ML-notes?color=66cdaa)](https://github.com/Sakura-gh/ML-notes/stargazers) [![GitHub license](https://img.shields.io/github/license/Sakura-gh/ML-notes?color=88cff1)](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

> 做了一段时间的笔记，发现真正去做project的时候，自己还是很生疏的，machine learning理论学习得再详细，最终也还是要落于实践才行，这段时间我将陆续将自己所做的几个Assignment上传至github上，尽量注释详细，并使用多种方法进行对比验证

##### html链接：

[1_Introduction]( https://sakura-gh.gitee.io/ml-notes/ML-notes-html/1_Introduction.html)

[2_Regression Case Study]( https://sakura-gh.gitee.io/ml-notes/ML-notes-html/2_Regression-Case-Study.html)

[3_Regression demo(Adagrad)]( https://sakura-gh.gitee.io/ml-notes/ML-notes-html/3_Regression-demo(Adagrad).html)

[4_Where does the error come from](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/4_Where-does-the-error-come-from.html)

[5_Gradient Descent](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/5_Gradient-Descent.html)

[6_Classification](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/6_Classification.html)

[7_Logistic Regression](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/7_Logistic-Regression.html)

[8_Deep Learning](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/8_Deep-Learning.html)

[9_Backpropagation](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/9_Backpropagation.html)

[10_Keras](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/10_Keras.html)

[11_Convolutional Neural Network part1](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/11_Convolutional-Neural-Network-part1.html)

[12_Convolutional Neural Network part2](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/12_Convolutional-Neural-Network-part2.html)

[13_Tips for Deep Learning](https://sakura-gh.gitee.io/ml-notes/ML-notes-html/13_Tips-for-Deep-Learning.html)

[14_Why Deep](https://sakura-gh.gitee.io/ml-notes/ML-notes/ML-notes-html/14_Why-Deep.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)

##### 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)](/code/手写数字识别(Keras)/digits-detection.py)

[手写数字识别CNN实现(Keras2.0)](/code/手写数字识别(Keras)/digits-detection-cnn.py)

##### 温馨提示：

图片加载可能会有些许缓慢，请耐心等待\\(\^o\^)/