Machine Learning && Deep Learning Resources
来源:互联网 发布:c 键值对数组 编辑:程序博客网 时间:2024/05/18 03:25
GitHub Special: Data Scientists to Follow & Best Tutorials on GitHub
Data Science Tutorials on GitHub
Now,if you are new to GitHub,you would be asking,where do tutorials come in on a platform meant for version control and sharing of codes.Well,because of its niche community,a lot of people have started creating resource repositories on GitHub. Essentially,since the programmers spend a lot of time on GitHub,why not create list of resources they use regularly.
Here’s a compiled list of tutorials on various topics in data science.These resources can be very handy.I suggest you to bookmark these(or watch these on GitHub).
1.Getting started with Data Science
Awesome Data Science:This is an awesome repository if you are to begin with Data Science.Here you’ll find every step that you need to take till the end of your journey.
Data Science Resources:This is another repository of Data Science Tutorials to help you conquer this skill set.You can free to choose any of these,both are equally good.
Text Books in Data Science:If you like to read and refer to books,here is a compiled list of best books on Machine Learning,Data Mining,Statistics,Data Visualization etc.
2.Algorithms
Data Science Algorithms:Here’s a comprehensive overview & explanation of algorithms such as Linear Regression,Logistic Regression,K-Mean Clustering,Random Forest.You’ll also find their worksheets for practice.
Statistics and ML:Here’s a list of tutorials to become efficient in your day to day programming.It covers python pandas,machine learning algorithms,statistics and data visualization.
3.Machine Learning
Scikit Learn:Scikit learn is a python library for machine learning. This repository has everything to offer to help you learn about machine learning in Python.(Hint:Dig Deeper)
Awesome Machine Learning:Here is an ultimate list of tutorials,resources,guides for Machine Learning,Data Analysis,Natural Language Processing,Data Visualization in all the programming languages like Python,R,Java,Go,C++,Swift.Choose accordingly.
Complete Machine Learning:Here’s a collection of tutorials and examples for solving problems using Machine Learning.It consist of beginning to end steps of ML covering stages such as model evaluation,implementation of ML algorithms,data visualization etc.
Parallel Machine Learning:This tutorial is on using scikit learn and iPython for parallel machine learning.Here you’ll find a 2 hours long video from Pycon 2013 with lecture notes and other useful resources.
Machine Learning Cources:Here’s a list of Best Machine Learning Cources in the world.
4.Deep Learning
Caffe:Caffe is a deep learning framework made with expression,speed,and modularity in mind.This repository consist of installation instructions and other recommended tutorials to help you learn this framework properly.
Awesome Deep Learning:Here’s a curated list of tutorials on Deep Learning which includes deep learning courses,free books,videos and lectures,papers and other useful resources to follow.
Deep Learning in Python:Here’s a complete tutorial on implementation of Deep Learning in Python.
Deep Learning in Julia:Mocha is a Deep Learning framework for Julia.This tutorial follows a step by step methodology to be able to introduce this framework in the best possible manner.
Recurrent Neural Networks:Here’s a awesome list of dedicated resources for RNN.If you have longed to curate the resources for RNN,you’ve like to stop here and take a glance.This guide consists of codes,lectures,book and resources on multiple applications of RNN.
Top 30 Data Scientists to Follow on GitHub
Here’s a compiled list of most influential data scientists on GitHub to follow.These data scientists are experts in their respective field which ranges from python,machine learning,neural nets,data visualization,deep learning,data science etc.
1.Sebastian Raschka (Machine Learning,Data Visualization)
2.Randy Olson (Python-Data Analysis,Matplotlib,Bokeh)
3.Hilary Mason (Chief Data Scientists at Bitly)
4.Mike Bostock (D3,Data Visualization)
5.Prakhar Srivastav (Python,Algorithms)
6.Andreas Mueller (Machine Learning,Python)
7.Wes Mckinney (Author of Python for Data Analysis)
8.Jake Vanderplas (Machine Learning,Data Visualization)
9.Mathieu Blondel (Machine Learning,Neural Networks)
10.Gael Varoquaux (Machine Learning,Statistics,Python)
11.Oliver Grisel (Machine Learning,Deep Learning)
12.Andrej (Deep Learning,Neural Networks,SVM)
13.Micheal Nielsen (Neural Networks,Deep Learning)
14.Heather Arthur (Neural Networks,Javascript)
15.Allen Downey (Python,Algorithms)
16.Davies Liu (Apache Spark,Python)
17.Julia Evans (Machine Learning,Python)
18.Jeff L (R Programming,Data Analysis)
19.John Myles White (Julia,Machine Learning)
20.Thomas Wiecki (Python,Bayesian Analysis)
21.Brian Caffo (John Hopkins University)
22.Roger D Peng (John Hopkins University)
23.Stefan Karpinski (Julia)
24.Pete Skomoroch (Machine Learning,Big Data,Python)
25.Mike Dewar (Python,D3,Javascript)
26.Hadley Wickham (Statistics,Data Analysis,Data Visualization)
27.Romain Francois (R Programming)
28.Justin Palmer (D3,Data Visualization)
29.Jason Davies (D3,Data Visualization)
30.Cameron Davidson Pilon (Python,Algorithms)
[转]http://www.analyticsvidhya.com/blog/2015/07/github-special-data-scientists-to-follow-best-tutorials/
- Machine Learning && Deep Learning Resources
- Deep Learning & Machine Learning
- Deep Learning Resources
- Deep Learning Resources
- Deep Learning Resources
- Deep Learning Resources
- Machine Learning Resources
- Deep Learning,Artificial Learning, Machine Learning
- Deep-Learning NotePad1 : Machine-Learning to Deep-Learning
- Machine Learning & Deep Learning 论文阅读笔记
- Machine Learning Resources机器学习的资料
- Best Machine Learning Resources for Getting Started
- Deep Machine Learning libraries and frameworks
- Deep Learning 的挑战: Extreme Learning Machine(超限学习机)?
- 机器学习(Machine Learning)&深度学习(Deep Learning)资料
- 机器学习(Machine Learning)&深度学习(Deep Learning)资料
- 机器学习(Machine Learning)&深度学习(Deep Learning)资料
- 机器学习(Machine Learning)&深度学习(Deep Learning)资料
- 2705 跳绳游戏
- U-boot分析
- 进制转换
- Android实战简易教程-第二十四枪(基于Baas的用户表查询功能实现!)
- MyEclipse 10, 2013, 2014 破解、注册码
- Machine Learning && Deep Learning Resources
- UVA_10006_CarmichaelNumbers
- POJ 1952 BUY LOW,BUY LOWER 最长递减子序列 动态规划
- 【PHP代码】生成百度短链接
- *Android 多线程下载 仿下载助手
- 南阳 oj NYoj 数据结构 最小数 题目1073
- Hadoop虚拟机固定Ip上网
- hdu 3038 How Many Answers Are Wrong
- C++异常处理学习记录