Tutorials

repo: ujjwalkarn/Machine-Learning-Tutorials
category: Computer Science

Machine Learning & Deep Learning Tutorials

• 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](https://github.com/ujjwalkarn/DataScienceR).

• [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython).

Contents

• Introduction
• 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
  • Cross Validation
  • Bootstraping
• Deep Learning
  • Frameworks
  • Feed Forward Networks
  • Recurrent Neural Nets, LSTM, GRU
  • Restricted Boltzmann Machine, DBNs
  • Autoencoders
  • Convolutional Neural Nets
  • Graph Representation Learning
• Natural Language Processing
  • Topic Modeling, LDA
  • Word2Vec
• 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

<a name="general" />

Introduction

• [Machine Learning Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning)

• AI/ML YouTube Courses

• [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml)

• [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)

• An Introduction to Statistical Learning

• [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning)

• [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

• Dive into Machine Learning

• [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning)

• [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz)

• [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience)

• [The Open Source Data Science Masters](http://datasciencemasters.org/)

• [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning)

• [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why)

• Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables

• [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts)

• [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations)

• [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html)

• [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)

• [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/)

• [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l)

• [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/)

• TheAnalyticsEdge edX Notes and Codes

• Have Fun With Machine Learning

• Twitter's Most Shared #machineLearning Content From The Past 7 Days

• Grokking Machine Learning

<a name="interview" />

Interview Resources

• [41 Essential Machine Learning Interview Questions (with answers)](https://www.springboard.com/blog/machine-learning-interview-questions/)

• How can a computer science graduate student prepare himself for data scientist interviews?

• [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1)

• [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq)

• What are the key skills of a data scientist?

• The Big List of DS/ML Interview Resources

<a name="ai" />

Artificial Intelligence

• [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence)

• UC Berkeley CS188 Intro to AI, Lecture Videos, 2

• [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai)

• [MIT 6.034 Artificial Intelligence Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), Complete Course

• edX course | Klein & Abbeel

• Udacity Course | Norvig & Thrun

• [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen)

<a name="ga" />

Genetic Algorithms

• [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm)

• [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/miniga.html), Part 2

• [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks)

• [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html)

• Genetic Programming

  • [Genetic Programming in Python (GitHub)](https://github.com/trevorstephens/gplearn)

  • Genetic Alogorithms vs Genetic Programming (Quora), StackOverflow

<a name="stat" />

Statistics

• 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

• What is a Sampling Distribution?

• Tutorials

  • AP Statistics Tutorial

  • Statistics and Probability Tutorial

  • Matrix Algebra Tutorial

• What is an Unbiased Estimator?

• Goodness of Fit Explained

• What are QQ Plots?

• OpenIntro Statistics - Free PDF textbook

<a name="blogs" />

Useful Blogs

• 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](http://timdettmers.com/) - 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

<a name="quora" />

Resources on Quora

• [Most Viewed Machine Learning writers](https://www.quora.com/topic/Machine-Learning/writers)

• Data Science Topic on Quora

• William Chen's Answers

• Michael Hochster's Answers

• Ricardo Vladimiro's Answers

• Storytelling with Statistics

• Data Science FAQs on Quora

• [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq)

<a name="kaggle" />

Kaggle Competitions WriteUp

• How to almost win Kaggle Competitions

• Convolution Neural Networks for EEG detection

• Facebook Recruiting III Explained

• Predicting CTR with Online ML

• How to Rank 10% in Your First Kaggle Competition

<a name="cs" />

Cheat Sheets

• Probability Cheat Sheet, Source

• [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)

• ML Compiled

<a name="classification" />

Classification

• Does Balancing Classes Improve Classifier Performance?

• What is Deviance?

• [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier)

• [What are the advantages of different classification algorithms?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms)

• ROC and AUC Explained (related video)

• An introduction to ROC analysis

• Simple guide to confusion matrix terminology

<a name="linear" />

Linear Regression

• General

  • Assumptions of Linear Regression, Stack Exchange

  • Linear Regression Comprehensive Resource

  • Applying and Interpreting Linear Regression

  • What does having constant variance in a linear regression model mean?

  • Difference between linear regression on y with x and x with y

  • Is linear regression valid when the dependant variable is not normally distributed?

• Multicollinearity and VIF

  • Dummy Variable Trap | Multicollinearity

  • Dealing with multicollinearity using VIFs

• Residual Analysis

  • Interpreting plot.lm() in R

  • How to interpret a QQ plot?

  • Interpreting Residuals vs Fitted Plot

• Outliers

  • How should outliers be dealt with?

• Elastic Net

  • Regularization and Variable Selection via the Elastic Net

<a name="logistic" />

Logistic Regression

• Logistic Regression Wiki

• Geometric Intuition of Logistic Regression

• Obtaining predicted categories (choosing threshold)

• Residuals in logistic regression

• Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki

• Pseudo R2 for Logistic Regression, How to calculate, Other Details

• Guide to an in-depth understanding of logistic regression

<a name="validation" />

Model Validation using Resampling

• Resampling Explained

• Partioning data set in R

• Implementing hold-out Validaion in R, 2

<a name="cross" />

• Cross Validation

  • How to use cross-validation in predictive modeling

  • Training with Full dataset after CV?

  • Which CV method is best?

  • Variance Estimates in k-fold CV

  • Is CV a subsitute for Validation Set?

  • Choice of k in k-fold CV

  • CV for ensemble learning

  • k-fold CV in R

  • Good Resources

  • Overfitting and Cross Validation

    • Preventing Overfitting the Cross Validation Data | Andrew Ng

    • Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

    • CV for detecting and preventing Overfitting

    • How does CV overcome the Overfitting Problem

<a name="boot" />

• Bootstrapping

  • Why Bootstrapping Works?

  • Good Animation

  • Example of Bootstapping

  • Understanding Bootstapping for Validation and Model Selection

  • Cross Validation vs Bootstrap to estimate prediction error, Cross-validation vs .632 bootstrapping to evaluate classification performance

<a name="deep" />

Deep Learning

• [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/)

• [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html)

• [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning)

• [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)

• [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html)

• [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), Website

• Core Concepts of Deep Learning

• Understanding Natural Language with Deep Neural Networks Using Torch

• [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/)

• [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq)

• [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725)

• Recent Reddit AMAs related to Deep Learning, Another AMA

• [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html)

• [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/)

• [Introduction to Deep Learning Using Python (GitHub)](https://github.com/rouseguy/intro2deeplearning), Good Introduction Slides

• Video Lectures Oxford 2015, Video Lectures Summer School Montreal

• [Deep Learning Software List](http://deeplearning.net/software_links/)

• Hacker's guide to Neural Nets

• [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html)

• Geoff Hinton Youtube Vidoes on Deep Learning

• [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/)

• [Deep Learning Comprehensive Website](http://deeplearning.net/), Software

• deeplearning Tutorials

• [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)

• [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)

• Intuition Behind Backpropagation

• Stanford Tutorials

• Train, Validation & Test in Artificial Neural Networks

• Artificial Neural Networks Tutorials

• Neural Networks FAQs on Stack Overflow

• [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html)

• [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/)

• Neural Machine Translation

  • Machine Translation Reading List

  • Introduction to Neural Machine Translation with GPUs (part 1), Part 2, Part 3

  • Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning

<a name="frame" />

• Deep Learning Frameworks

  • Torch vs. Theano

  • dl4j vs. torch7 vs. theano

  • [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/)

  • Theano

    • Website

    • Theano Introduction

truncated — full list on GitHub