This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.
If you want to contribute to this list, please read 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.
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
41 Essential Machine Learning Interview Questions (with answers)
How can a computer science graduate student prepare himself for data scientist interviews?
Programming Community Curated Resources for learning Artificial Intelligence
MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course
Stat Trek Website - A dedicated website to teach yourselves Statistics
Learn Statistics Using Python - Learn Statistics using an application-centric programming approach
Statistics for Hackers | Slides | @jakevdp - Slides by Jake VanderPlas
Online Statistics Book - An Interactive Multimedia Course for Studying Statistics
Tutorials
OpenIntro Statistics - Free PDF textbook
Edwin Chen's Blog - A blog about Math, stats, ML, crowdsourcing, data science
The Data School Blog - Data science for beginners!
ML Wave - A blog for Learning Machine Learning
Andrej Karpathy - A blog about Deep Learning and Data Science in general
Colah's Blog - Awesome Neural Networks Blog
Alex Minnaar's Blog - A blog about Machine Learning and Software Engineering
Statistically Significant - Andrew Landgraf's Data Science Blog
Simply Statistics - A blog by three biostatistics professors
Yanir Seroussi's Blog - A blog about Data Science and beyond
fastML - Machine learning made easy
Trevor Stephens Blog - Trevor Stephens Personal Page
no free hunch | kaggle - The Kaggle Blog about all things Data Science
A Quantitative Journey | outlace - learning quantitative applications
r4stats - analyze the world of data science, and to help people learn to use R
Variance Explained - David Robinson's Blog
AI Junkie - a blog about Artificial Intellingence
Deep Learning Blog by Tim Dettmers - Making deep learning accessible
J Alammar's Blog- Blog posts about Machine Learning and Neural Nets
Adam Geitgey - Easiest Introduction to machine learning
Ethen's Notebook Collection - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
Multicollinearity and VIF
Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
Pseudo R2 for Logistic Regression, How to calculate, Other Details
Overfitting and Cross Validation
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
Neural Machine Translation
Deep Learning Frameworks
The Unreasonable effectiveness of RNNs, Torch Code, Python Code
Long Short Term Memory (LSTM)
Gated Recurrent Units (GRU)
Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
A curated list of speech and natural language processing resources
Understanding Natural Language with Deep Neural Networks Using Torch
word2vec
Text Clustering
Text Classification
Named Entity Recognitation
Kaggle Tutorial Bag of Words and Word vectors, Part 2, Part 3
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 - Grow and plot a decision tree to automatically figure out hidden rules in your data
Comparison of Different Algorithms
CART
CTREE
CHAID
MARS
Probabilistic Decision Trees
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
Why doesn't Random Forest handle missing values in predictors?
Mean Variance Portfolio Optimization with R and Quadratic Programming
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
For a collection of Data Science Tutorials using R, please refer to this list.
For a collection of Data Science Tutorials using Python, please refer to this list.
理解机器学习-经典教程 (从门外汉开始): https://www.bilibili.com/video/BV164411b7dx?from=search&seid=9555862252436536197 Andrew Ng-机器学习入门 https://www.bilibili.com/video/BV11t411A7Ym?from=search&seid=870997702028386837
莫烦python的强化学习教程中tabular Q-learning小例子是一个一维的空间,原文链接https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-1-general-rl/,本文将其改进为二维空间,代码如下. # -*- coding: utf-8 -*- """ Created o
Mnist分类器 一、数据准备 从tensorflow下载mnist数据 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets('/content',one_hot=True) 查看训练数据的大小 print(mnist.
学习意味着通过学习或经验获得知识或技能。 基于此,我们可以定义机器学习(ML)如下 - 它可以被定义为计算机科学领域,更具体地说是人工智能的应用,其为计算机系统提供了学习数据和从经验改进而无需明确编程的能力。 基本上,机器学习的主要焦点是允许计算机自动学习而无需人为干预。 现在问题是如何开始和完成这种学习? 它可以从数据的观察开始。 数据可以是一些示例,指令或一些直接经验。 然后在此输入的基础上,
Machine Learning This project provides a web-interface,as well as a programmatic-apifor various machine learning algorithms. Supported algorithms: Support Vector Machine (SVM) Support Vector Regressio
深度学习 我们可以在Personal Computer上完成庞大的任务 深度学习是一种适应于各类问题的万能药 神经网络 神经网络出现于80年代,但当时计算机运行慢,数据集很小,神经网络不适用 现在神经网络回来了,因为能够进行GPU计算,可用使用的数据集也变大 分类 分类的一些讨论可以在这个项目里看到 Machine Learning不仅是Classification!但分类是机器学习的核心。 学会
Machine Learning Projects This repository contains mini projects in machine learning with jupyter notebook files.Go to the projects folder and see the readme for detailed instructions about the projec
Machine Learning for OpenCV This is the Jupyter notebook version of the following book: Michael Beyeler Machine Learning for OpenCV Intelligent Image Processing with Python 14 July 2017 Packt Publishi
Machine Learning and Data Science Applications in Industry Sov.ai Research Lab (Sponsorship) Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data S