Hello-Machine-Learning

本页汇集了机器学习相关的理论和实践学习内容。

我的机器学习笔记

机器学习框架

当前流行的机器学习框架，Scikit-learn, tensorflow, xgboost, keras, NLT, gensim, numpy, 基本上都能使用Python Anaconda安装。

TensorFlow https://lucky521.github.io/blog/machinelearning/2017/10/26/tensorflow.html

XGBoost https://lucky521.github.io/blog/machinelearning/2018/03/25/boosting.html

Scikit-Learn https://lucky521.github.io/blog/machinelearning/2016/12/28/scikit-learn.html

机器学习方法

特征工程 https://lucky521.github.io/blog/machinelearning/2018/04/18/feature-engineering.html

模型评价 https://lucky521.github.io/blog/machinelearning/2017/01/01/metrics-to-evaluate-model.html

提升方法 https://lucky521.github.io/blog/machinelearning/2018/03/25/boosting.html

最优化方法 https://lucky521.github.io/blog/machinelearning/2018/07/31/optimization-method.html

深度学习 https://lucky521.github.io/blog/machinelearning/2017/06/14/deep-learning.html

CNN网络 https://lucky521.github.io/blog/machinelearning/2017/12/21/cnn.html

垂直领域应用

图像分类 https://lucky521.github.io/blog/machinelearning/2017/03/27/image-recognition.html

深度学习实践之OpenCV https://lucky521.github.io/blog/framework/2017/12/01/opencv.html

自然语言处理 https://lucky521.github.io/blog/machinelearning/2018/05/15/nlp-using-machine-learning.html

搜索排序 https://lucky521.github.io/blog/tech/2018/02/23/search-tech.html

个性化搜索+个性化推荐 https://lucky521.github.io/blog/tech/2018/04/05/personalization-algorithm.html

相关Repro

当前项目 https://github.com/lucky521/Hello-Machine-Learning

深度学习 https://github.com/lucky521/deep-learning

视觉图像 https://github.com/lucky521/your-face

机器学习资料整理

资料门户

deeplearning.net

Machine Learning Road

资料书籍

应用实践类书籍

李沐《动手学深度学习》 https://zh.gluon.ai/

Andrew Ng 《Machine Learning Yearning》

Rules of Machine Learning: Best Practices for ML Engineering

Michael Nielsen的神经网络入门资料

University of Montreal LISA lab 的深度学习教材

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Python Machine Learning

Deep learning with python

理论类书籍

李航《统计学习方法》

周志华《机器学习》

Ian Goodfellow 《Deep Learning》

Machine Learning:A Probabilistic Perspective

Pattern Recognition and Machine Learning

The Elements of Statistical Learning

《神经网络与深度学习》(https://nndl.github.io/nndl-book.pdf)

经典模型/经典方案论文

深度学习经典论文集1

深度学习经典论文集2

神经网络架构模型图谱

机器学习问题与解决方案集

推荐系统论文收集1

推荐系统论文收集2

公开课

基础课程

机器学习速成课程

https://developers.google.com/machine-learning/crash-course/?hl=zh-cn

机器学习基础

https://bloomberg.github.io/foml/#home

林轩田 《机器学习基石》和《机器学习技法》

机器学习技术课程笔记 - 台湾大学林轩田

斯坦福 CS229 Machine Learning

有三个两个版本 https://www.coursera.org/learn/machine-learning

MACHINE LEARNING YEARNING

https://github.com/AcceptedDoge/machine-learning-yearning-cn

李宏毅机器学习课程

https://datawhalechina.github.io/leeml-notes/#/

分支课程

斯坦福 CS224N 自然语言处理nlp-with-deep-learning

https://web.stanford.edu/class/cs224n/

斯坦福 CS231n 深度学习与计算机视觉

http://cs231n.github.io/
2016版 http://study.163.com/course/courseMain.htm?courseId=1003223001
2017版 http://www.mooc.ai/course/268

斯坦福 MS&E239 广告课程

https://web.stanford.edu/class/msande239/

斯坦福 CS230 Deep Learning

http://cs230.stanford.edu/

deeplearning.ai

https://mooc.study.163.com/course/deeplearning_ai-2001281002#/info

信息图谱

机器学习的知识图谱

深度学习知识图谱

机器学习算法工程师技能图谱

机器学习速查表

https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

https://github.com/kailashahirwar/cheatsheets-ai

AI Cheatsheets

数学的深渊

实践比赛

赛事

http://www.kaggle.com 数据挖掘比赛。

比赛获奖的解决方案

http://ndres.me/kaggle-past-solutions/

https://github.com/geekinglcq/CDCS