---
visibility: public
---

# Tutorials

**repo:** [ujjwalkarn/Machine-Learning-Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)  
**category:** [[computer-science|Computer Science]]

---

# Machine Learning & Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

- This repository contains a topic-wise curated list of [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) and [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) tutorials, articles and other resources. Other [awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) lists can be found in this [list](https://github.com/sindresorhus/awesome).

- If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-[Learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-Tutorials/blob/master/contributing.md).

- [Curated list of R tutorials for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science), NLP and Machine Learning](https://github.com/ujjwalkarn/DataScienceR).

- [Curated list of [Python](/@harrisonqian/awesome/wiki/programming-languages/python) tutorials for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science), NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython).

## Contents
- [Introduction](#general)
- [Interview Resources](#interview)
- [Artificial Intelligence](#ai)
- [Genetic Algorithms](#ga)
- [Statistics](#stat)
- [Useful Blogs](#blogs)
- [Resources on Quora](#quora)
- [Resources on Kaggle](#kaggle)
- [Cheat Sheets](#cs)
- [Classification](#classification)
- [Linear Regression](#linear)
- [Logistic Regression](#logistic)
- [Model Validation using Resampling](#validation)
    - [Cross Validation](#cross)
    - [Bootstraping](#boot)
- [Deep Learning](#deep)
    - [Frameworks](#frame)
    - [Feed Forward Networks](#feed)
    - [Recurrent Neural Nets, LSTM, GRU](#rnn)
    - [Restricted Boltzmann Machine, DBNs](#rbm)
    - [Autoencoders](#auto)
    - [Convolutional Neural Nets](#cnn)
    - [Graph Representation Learning](#nrl)
- [Natural Language Processing](#nlp)
    - [Topic Modeling, LDA](#topic)
    - [Word2Vec](#word2vec)
- [Computer Vision](#vision)
- [Support Vector Machine](#svm)
- [Reinforcement Learning](#rl)
- [Decision Trees](#dt)
- [Random Forest / Bagging](#rf)
- [Boosting](#gbm)
- [Ensembles](#ensem)
- [Stacking Models](#stack)
- [VC Dimension](#vc)
- [Bayesian Machine Learning](#bayes)
- [Semi Supervised Learning](#semi)
- [Optimizations](#opt)
- [Other Useful Tutorials](#other)

<a name="general" />

## Introduction

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning)

- [AI/ML YouTube Courses](https://github.com/dair-ai/ML-YouTube-Courses)

- [Curated List of [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Resources](https://hackr.io/tutorials/learn-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-ml)

- [In-depth introduction to [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-videos/)

- [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)

- [List of [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning)

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

- [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning)

- [A curated list of [awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) [frameworks](/@harrisonqian/awesome/wiki/front-end-development/frameworks), libraries and software](https://github.com/josephmisiti/awesome-machine-learning)

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

- [An [awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience)

- [The Open Source [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Masters](http://datasciencemasters.org/)

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning)

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/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](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf)

- [List of [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts)

- [Slides on Several [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Topics](http://www.slideshare.net/pierluca.lanzi/presentations)

- [MIT [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html)

- [Comparison Supervised [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)

- [Learning [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/)

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

- [Statistical [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Course](http://www.stat.cmu.edu/~larry/=sml/)

- [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge)

- [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning)

- [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning)

- [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning)

<a name="interview" />

## Interview Resources

- [41 Essential [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Interview Questions (with answers)](https://www.springboard.com/blog/machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-interview-questions/)

- [How can a computer science graduate student prepare himself for data scientist interviews?](https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-intern-interviews)

- [How do I learn [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning)?](https://www.quora.com/How-do-I-learn-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-1)

- [FAQs about [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq)

- [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist)

- [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63)

<a name="ai" />

## Artificial Intelligence

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

- [UC Berkeley CS188 Intro to AI](http://ai.berkeley.edu/home.html), [Lecture Videos](http://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM)

- [Programming Community Curated Resources for [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) 

- [MIT 6.034 [Artificial Intelligence](/@harrisonqian/awesome/wiki/theory/artificial-intelligence) Lecture Videos](https://www.youtube.com/playlist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)

- [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info)

- [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)

- [TED [talks](/@harrisonqian/awesome/wiki/theory/talks) on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen)

<a name="ga" />

## Genetic Algorithms

- [Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm)

- [Simple Implementation of Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) (Part 1)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html)

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

- [Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html)

- [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming)

    - [Genetic Programming in [Python](/@harrisonqian/awesome/wiki/programming-languages/python) (GitHub)](https://github.com/trevorstephens/gplearn)
    
    - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-[Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms)-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming)

<a name="stat" />

## Statistics

- [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics

- [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach

- [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas

- [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics

- [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx)

- Tutorials

    - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx)
    
    - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx)
    
    - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx)
    
- [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/)

- [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit)

- [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html)

- [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook

<a name="blogs" />

## Useful Blogs

- [Edwin Chen's Blog](http://blog.echen.me/) - A blog about [Math](/@harrisonqian/awesome/wiki/theory/math), stats, ML, crowdsourcing, [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science)

- [The Data School Blog](http://www.dataschool.io/) - [Data science](/@harrisonqian/awesome/wiki/programming-languages/data-science) for beginners!

- [ML Wave](http://mlwave.com/) - A blog for [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning)

- [Andrej Karpathy](http://karpathy.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - A blog about [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) and [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) in general

- [Colah's Blog](http://colah.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - [Awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) Neural Networks Blog

- [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) and Software Engineering

- [Statistically Significant](http://andland.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/) - Andrew Landgraf's [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Blog

- [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors

- [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and beyond

- [fastML](http://fastml.com/) - [Machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) made easy

- [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page

- [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science)

- [A Quantitative Journey | outlace](http://outlace.com/) -  [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) quantitative applications

- [r4stats](http://r4stats.com/) - analyze the world of [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science), and to help people learn to use R

- [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog

- [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Blog by Tim Dettmers](http://timdettmers.com/) - Making [deep learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) accessible

- [J Alammar's Blog](http://jalammar.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/)- Blog posts about [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) and Neural Nets

- [Adam Geitgey](https://medium.com/@ageitgey/machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning)

- [Ethen's Notebook Collection](https://github.com/ethen8181/machine-learning) - Continuously updated [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) documentations (mainly in Python3). Contents include educational implementation of machine [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) from scratch and open-source library usage

<a name="quora" />

## Resources on Quora

- [Most Viewed [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) writers](https://www.quora.com/topic/Machine-Learning/writers)

- [Data Science Topic on Quora](https://www.quora.com/Data-Science)

- [William Chen's Answers](https://www.quora.com/William-Chen-6/answers)

- [Michael Hochster's Answers](https://www.quora.com/Michael-Hochster/answers)

- [Ricardo Vladimiro's Answers](https://www.quora.com/Ricardo-Vladimiro-1/answers)

- [Storytelling with Statistics](https://datastories.quora.com/)

- [Data Science FAQs on Quora](https://www.quora.com/topic/Data-Science/faq)

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq)

<a name="kaggle" />

## Kaggle Competitions WriteUp

- [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/)

- [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/)

- [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html)

- [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/)

- [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/)

<a name="cs" />

## Cheat Sheets

- [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf),
[Source](http://www.wzchen.com/probability-cheatsheet/)

- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)

- [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/)

<a name="classification" />

## Classification

- [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/)

- [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart)

- [When to choose which [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-classifier)

- [What are the advantages of different classification [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms)?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms)

- [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) ([related video](https://youtu.be/OAl6eAyP-yo))

- [An introduction to ROC analysis](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf)

- [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)

<a name="linear" />

## Linear Regression

- [General](#general-)

    - [Assumptions of Linear Regression](http://pareonline.net/getvn.asp?n=2&v=8), [Stack Exchange](http://stats.stackexchange.com/questions/16381/what-is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
    
    - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm)
    
    - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/)
    
    - [What does having constant variance in a linear regression model mean?](http://stats.stackexchange.com/questions/52089/what-does-having-constant-variance-in-a-linear-regression-model-mean/52107?stw=2#52107)
    
    - [Difference between linear regression on y with x and x with y](http://stats.stackexchange.com/questions/22718/what-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1)
    
    - [Is linear regression valid when the dependant variable is not normally distributed?](https://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed)
- Multicollinearity and VIF

    - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity)
    
    - [Dealing with multicollinearity using VIFs](https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/)

- [Residual Analysis](#residuals-)

    - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm)
    
    - [How to interpret a QQ plot?](http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1)
    
    - [Interpreting Residuals vs Fitted Plot](http://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions)

- [Outliers](#outliers-)

    - [How should outliers be dealt with?](http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis)

- [Elastic Net](https://en.wikipedia.org/wiki/Elastic_net_regularization)
    - [Regularization and Variable Selection via the
Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)

<a name="logistic" />

## Logistic Regression

- [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression)

- [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html)

- [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit)

- [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean)

- [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model)

- [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm)

- [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/)

<a name="validation" />

## Model Validation using Resampling

- [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics))

- [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations)

- [Implementing hold-out Validaion in R](http://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r), [2](http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-[testing](/@harrisonqian/awesome/wiki/testing/testing)-and.html)

<a name="cross" />

- [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
    - [How to use cross-validation in predictive modeling](http://stuartlacy.co.uk/2016/02/04/how-to-correctly-use-cross-validation-in-predictive-modelling/)
    - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation)
    
    - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best)
    
    - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation)
    
    - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set)
    
    - [Choice of k in k-fold CV](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation)
    
    - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning)
    
    - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret)
    
    - [Good Resources](http://www.chioka.in/tag/cross-validation/)
    
    - Overfitting and Cross Validation
    
        - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf)
        
        - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf)

        - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf)
        
        - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem)

<a name="boot" />

- [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))

    - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works)
    
    - [Good Animation](https://www.stat.auckland.ac.nz/~wild/BootAnim/)
    
    - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm)
    
    - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1)
    
    - [Cross Validation vs Bootstrap to estimate prediction error](http://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)

<a name="deep" />

## Deep Learning

- [fast.ai - Practical [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) For Coders](http://course.fast.ai/)

- [fast.ai - Cutting Edge [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) For Coders](http://course.fast.ai/part2.html)

- [A curated list of [awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning)

- **[Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) [Papers](/@harrisonqian/awesome/wiki/computer-science/papers) Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)**

- [Lots of [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Resources](http://deeplearning4j.org/documentation.html)

- [Interesting [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/)

- [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-nutshell-[core](/@harrisonqian/awesome/wiki/platforms/core)-concepts/)

- [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/)

- [Stanford [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Tutorial](http://ufldl.stanford.edu/tutorial/)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq)

- [Google+ [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Page](https://plus.google.com/communities/112866381580457264725)

- [Recent Reddit AMAs related to Deep Learning](http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/), [Another AMA](https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/)

- [Where to Learn [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning)?](http://www.kdnuggets.com/2014/05/learn-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-courses-tutorials-overviews.html)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-[core](/@harrisonqian/awesome/wiki/platforms/core)-concepts/)

- [Introduction to [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Using [Python](/@harrisonqian/awesome/wiki/programming-languages/python) (GitHub)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning)

- [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Software List](http://deeplearning.net/software_links/)

- [Hacker's guide to Neural Nets](http://karpathy.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/neuralnets/)

- [Top arxiv [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) [Papers](/@harrisonqian/awesome/wiki/computer-science/papers) explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-papers-explained.html)

- [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA)

- [Awesome [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Reading List](http://deeplearning.net/reading-list/)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/)

- [deeplearning Tutorials](http://deeplearning4j.org/)

- [AWESOME! [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-from-perceptrons-to-deep-networks)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)

- [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c)

- [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/)

- [Train, Validation & Test in Artificial Neural Networks](http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ)

- [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-about-artificial-neural-networks)

- [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50)

- [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html)

- [Neural Networks and [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Online Book](http://neuralnetworksanddeeplearning.com/)

- Neural Machine Translation

    - **[Machine Translation Reading List](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list)**

    - [Introduction to Neural Machine Translation with GPUs (part 1)](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/), [Part 2](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/)
    
    - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/)

<a name="frame" />

- [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) [Frameworks](/@harrisonqian/awesome/wiki/front-end-development/frameworks)

    - [Torch vs. Theano](http://fastml.com/torch-vs-theano/)
    
    - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html)
    
    - [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/)
    

    - [Theano](https://en.wikipedia.org/wiki/Theano_(software))
    
        - [Website](http://deeplearning.net/software/theano/)
        
        - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/)
        

---
*truncated — [full list on GitHub](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)*