教程
Machine Learning & Deep Learning Tutorials
¶
-
此存储库包含主题明确的机器学习和深度学习教程列表,文章和其他资源. 其他很棒的列表可以在这里找到 list.
-
如果您想参与此列表,请阅读 Contributing Guidelines.
-
Curated list of R tutorials for Data Science, NLP and Machine Learning.
-
Curated list of Python tutorials for Data Science, NLP and Machine Learning.
- Interview Resources
- Artificial Intelligence
- Genetic Algorithms
- Statistics
- Useful Blogs
- Resources on Quora
- Resources on Kaggle
- Cheat Sheets
- Classification
- Linear Regression
- Logistic Regression
- Model Validation using Resampling
- Deep Learning
- Natural Language Processing
- Computer Vision
- Support Vector Machine
- Reinforcement Learning
- Decision Trees
- Random Forest / Bagging
- Boosting
- Ensembles
- Stacking Models
- VC Dimension
- Bayesian Machine Learning
- Semi Supervised Learning
- Optimizations
- Other Useful Tutorials
Introduction¶
-
In-depth introduction to machine learning in 15 hours of expert videos
-
A curated list of awesome Machine Learning frameworks, libraries and software
-
A curated list of awesome data visualization libraries and resources.
-
An awesome Data Science repository to learn and apply for real world problems
-
Machine Learning algorithms that you should always have a strong understanding of
-
Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
-
Twitter's Most Shared #machineLearning Content From The Past 7 Days
Interview Resources¶
-
41 Essential Machine Learning Interview Questions (with answers)
-
How can a computer science graduate student prepare himself for data scientist interviews?
Artificial Intelligence¶
-
Programming Community Curated Resources for learning Artificial Intelligence
-
MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course
Genetic Algorithms¶
Statistics¶
-
Stat Trek Website - 一个专门的网站,教你自己统计
-
Learn Statistics Using Python - 使用以应用程序为中心的编程方法学习统计数据
-
Statistics for Hackers | Slides | @jakevdp - Jake VanderPlas的幻灯片
-
Online Statistics Book - 研究统计学的互动多媒体课程
-
教程
-
OpenIntro Statistics - 免费的PDF教科书
Useful Blogs¶
-
Edwin Chen's Blog - 关于数学,统计,ML,众包,数据科学的博客
-
The Data School Blog - 面向初学者的数据科学!
-
ML Wave - 学习机器学习的博客
-
Andrej Karpathy - 一般关于深度学习和数据科学的博客
-
Colah's Blog - 令人敬畏的神经网络博客
-
Alex Minnaar's Blog - 关于机器学习和软件工程的博客
-
Statistically Significant - Andrew Landgraf的数据科学博客
-
Simply Statistics - 由三位生物统计学教授撰写的博客
-
Yanir Seroussi's Blog - 关于数据科学及其他内容的博客
-
fastML - 机器学习变得简单
-
Trevor Stephens Blog - Trevor Stephens个人主页
-
no free hunch | kaggle - 关于所有数据科学的Kaggle博客
-
A Quantitative Journey | outlace - 学习定量应用
-
r4stats - 分析数据科学的世界,并帮助人们学习使用R
-
Variance Explained - 大卫罗宾逊的博客
-
AI Junkie - 关于人工智能的博客
-
J Alammar's Blog- 关于机器学习和神经网络的博客文章
-
Adam Geitgey - 最简单的机器学习简介
-
Ethen's Notebook Collection - 不断更新的机器学习文档(主要在Python3中). 内容包括从头开始的机器学习算法的教育实现和开源库的使用
Resources on Quora¶
Kaggle Competitions WriteUp¶
Cheat Sheets¶
Classification¶
Linear Regression¶
-
- [正则化和变量选择通过 弹性网](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
Logistic Regression¶
-
Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
-
Pseudo R2 for Logistic Regression, How to calculate, Other Details
Model Validation using Resampling¶
Deep Learning¶
-
A curated list of awesome Deep Learning tutorials, projects and communities
-
Interesting Deep Learning and NLP Projects (Stanford), Website
-
Understanding Natural Language with Deep Neural Networks Using Torch
-
Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides
-
Video Lectures Oxford 2015, Video Lectures Summer School Montreal
-
神经机器翻译
-
深度学习框架
-
-
Caffe
-
TensorFlow
-
经常性和LSTM网络
-
受限制的玻尔兹曼机器
-
自动编码器:无监督(设置目标=输入后应用BackProp)
-
卷积神经网络
-
网络表示学习
Natural Language Processing¶
-
A curated list of speech and natural language processing resources
-
Understanding Natural Language with Deep Neural Networks Using Torch
-
word2vec
-
文本聚类
-
文本分类
-
命名实体认可
-
Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
Computer Vision¶
Support Vector Machine¶
-
比较
-
软件
-
内核
-
SVM发布概率
Reinforcement Learning¶
Decision Trees¶
-
What is entropy and information gain in the context of building decision trees?
-
How do decision tree learning algorithms deal with missing values?
-
Discover structure behind data with decision trees - 增长并绘制决策树,以自动计算数据中的隐藏规则
-
不同算法的比较
-
CART
-
CTREE
-
CHAID
-
三月
-
概率决策树
Random Forest / Bagging¶
-
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
-
Why doesn't Random Forest handle missing values in predictors?
Boosting¶
-
梯度增压机
-
xgboost
-
AdaBoost
-
CatBoost
Ensembles¶
Stacking Models¶
Vapnik–Chervonenkis Dimension¶
Bayesian Machine Learning¶
Semi Supervised Learning¶
Optimization¶
-
Mean Variance Portfolio Optimization with R and Quadratic Programming
-
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters