[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9597":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":37,"readmeContent":38,"aiSummary":39,"trendingCount":15,"starSnapshotCount":15,"syncStatus":40,"lastSyncTime":41,"discoverSource":42},9597,"Machine-Learning-Tutorials","ujjwalkarn\u002FMachine-Learning-Tutorials","ujjwalkarn","machine learning and deep learning tutorials, articles and other resources ","http:\u002F\u002Fujjwalkarn.github.io\u002FMachine-Learning-Tutorials",null,17890,3989,796,10,0,7,13,90,21,99.5,"Creative Commons Zero v1.0 Universal",false,"master",true,[26,27,28,29,30,31,32,33,34,35,36],"awesome","awesome-list","deep-learning","deep-learning-tutorial","deep-neural-networks","deeplearning","list","machine-learning","machinelearning","neural-network","neural-networks","2026-06-12 04:00:45","\n# Machine Learning & Deep Learning Tutorials [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n\n- 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](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome).\n\n- If you want to contribute to this list, please read [Contributing Guidelines](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials\u002Fblob\u002Fmaster\u002Fcontributing.md).\n\n- [Curated list of R tutorials for Data Science, NLP and Machine Learning](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataScienceR).\n\n- [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FDataSciencePython).\n\n\n## Contents\n- [Introduction](#general)\n- [Interview Resources](#interview)\n- [Artificial Intelligence](#ai)\n- [Genetic Algorithms](#ga)\n- [Statistics](#stat)\n- [Useful Blogs](#blogs)\n- [Resources on Quora](#quora)\n- [Resources on Kaggle](#kaggle)\n- [Cheat Sheets](#cs)\n- [Classification](#classification)\n- [Linear Regression](#linear)\n- [Logistic Regression](#logistic)\n- [Model Validation using Resampling](#validation)\n    - [Cross Validation](#cross)\n    - [Bootstraping](#boot)\n- [Deep Learning](#deep)\n    - [Frameworks](#frame)\n    - [Feed Forward Networks](#feed)\n    - [Recurrent Neural Nets, LSTM, GRU](#rnn)\n    - [Restricted Boltzmann Machine, DBNs](#rbm)\n    - [Autoencoders](#auto)\n    - [Convolutional Neural Nets](#cnn)\n    - [Graph Representation Learning](#nrl)\n- [Natural Language Processing](#nlp)\n    - [Topic Modeling, LDA](#topic)\n    - [Word2Vec](#word2vec)\n- [Computer Vision](#vision)\n- [Support Vector Machine](#svm)\n- [Reinforcement Learning](#rl)\n- [Decision Trees](#dt)\n- [Random Forest \u002F Bagging](#rf)\n- [Boosting](#gbm)\n- [Ensembles](#ensem)\n- [Stacking Models](#stack)\n- [VC Dimension](#vc)\n- [Bayesian Machine Learning](#bayes)\n- [Semi Supervised Learning](#semi)\n- [Optimizations](#opt)\n- [Other Useful Tutorials](#other)\n\n\u003Ca name=\"general\" \u002F>\n\n## Introduction\n\n- [Machine Learning Course by Andrew Ng (Stanford University)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)\n\n- [AI\u002FML YouTube Courses](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FML-YouTube-Courses)\n\n- [Curated List of Machine Learning Resources](https:\u002F\u002Fhackr.io\u002Ftutorials\u002Flearn-machine-learning-ml)\n\n- [In-depth introduction to machine learning in 15 hours of expert videos](http:\u002F\u002Fwww.dataschool.io\u002F15-hours-of-expert-machine-learning-videos\u002F)\n\n- [An Introduction to Statistical Learning](http:\u002F\u002Fwww-bcf.usc.edu\u002F~gareth\u002FISL\u002F)\n\n- [List of Machine Learning University Courses](https:\u002F\u002Fgithub.com\u002Fprakhar1989\u002Fawesome-courses#machine-learning)\n\n- [Machine Learning for Software Engineers](https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers)\n\n- [Dive into Machine Learning](https:\u002F\u002Fgithub.com\u002Fhangtwenty\u002Fdive-into-machine-learning)\n\n- [A curated list of awesome Machine Learning frameworks, libraries and software](https:\u002F\u002Fgithub.com\u002Fjosephmisiti\u002Fawesome-machine-learning)\n\n- [A curated list of awesome data visualization libraries and resources.](https:\u002F\u002Fgithub.com\u002Ffasouto\u002Fawesome-dataviz)\n\n- [An awesome Data Science repository to learn and apply for real world problems](https:\u002F\u002Fgithub.com\u002Fokulbilisim\u002Fawesome-datascience)\n\n- [The Open Source Data Science Masters](http:\u002F\u002Fdatasciencemasters.org\u002F)\n\n- [Machine Learning FAQs on Cross Validated](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Fmachine-learning)\n\n- [Machine Learning algorithms that you should always have a strong understanding of](https:\u002F\u002Fwww.quora.com\u002FWhat-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why)\n\n- [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http:\u002F\u002Fterpconnect.umd.edu\u002F~bmomen\u002FBIOM621\u002FLineardepCorrOrthogonal.pdf)\n\n- [List of Machine Learning Concepts](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FList_of_machine_learning_concepts)\n\n- [Slides on Several Machine Learning Topics](http:\u002F\u002Fwww.slideshare.net\u002Fpierluca.lanzi\u002Fpresentations)\n\n- [MIT Machine Learning Lecture Slides](http:\u002F\u002Fwww.ai.mit.edu\u002Fcourses\u002F6.867-f04\u002Flectures.html)\n\n- [Comparison Supervised Learning Algorithms](http:\u002F\u002Fwww.dataschool.io\u002Fcomparing-supervised-learning-algorithms\u002F)\n\n- [Learning Data Science Fundamentals](http:\u002F\u002Fwww.dataschool.io\u002Flearning-data-science-fundamentals\u002F)\n\n- [Machine Learning mistakes to avoid](https:\u002F\u002Fmedium.com\u002F@nomadic_mind\u002Fnew-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l)\n\n- [Statistical Machine Learning Course](http:\u002F\u002Fwww.stat.cmu.edu\u002F~larry\u002F=sml\u002F)\n\n- [TheAnalyticsEdge edX Notes and Codes](https:\u002F\u002Fgithub.com\u002Fpedrosan\u002FTheAnalyticsEdge)\n\n- [Have Fun With Machine Learning](https:\u002F\u002Fgithub.com\u002Fhumphd\u002Fhave-fun-with-machine-learning)\n\n- [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http:\u002F\u002Ftheherdlocker.com\u002Ftweet\u002Fpopularity\u002Fmachinelearning)\n\n- [Grokking Machine Learning](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fgrokking-machine-learning)\n\n\u003Ca name=\"interview\" \u002F>\n\n## Interview Resources\n\n- [41 Essential Machine Learning Interview Questions (with answers)](https:\u002F\u002Fwww.springboard.com\u002Fblog\u002Fmachine-learning-interview-questions\u002F)\n\n- [How can a computer science graduate student prepare himself for data scientist interviews?](https:\u002F\u002Fwww.quora.com\u002FHow-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews)\n\n- [How do I learn Machine Learning?](https:\u002F\u002Fwww.quora.com\u002FHow-do-I-learn-machine-learning-1)\n\n- [FAQs about Data Science Interviews](https:\u002F\u002Fwww.quora.com\u002Ftopic\u002FData-Science-Interviews\u002Ffaq)\n\n- [What are the key skills of a data scientist?](https:\u002F\u002Fwww.quora.com\u002FWhat-are-the-key-skills-of-a-data-scientist)\n\n- [The Big List of DS\u002FML Interview Resources](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-big-list-of-ds-ml-interview-resources-2db4f651bd63)\n\n\u003Ca name=\"ai\" \u002F>\n\n## Artificial Intelligence\n\n- [Awesome Artificial Intelligence (GitHub Repo)](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence)\n\n- [UC Berkeley CS188 Intro to AI](http:\u002F\u002Fai.berkeley.edu\u002Fhome.html), [Lecture Videos](http:\u002F\u002Fai.berkeley.edu\u002Flecture_videos.html), [2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=W1S-HSakPTM)\n\n- [Programming Community Curated Resources for learning Artificial Intelligence](https:\u002F\u002Fhackr.io\u002Ftutorials\u002Flearn-artificial-intelligence-ai) \n\n- [MIT 6.034 Artificial Intelligence Lecture Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi), [Complete Course](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-034-artificial-intelligence-fall-2010\u002F)\n\n- [edX course | Klein & Abbeel](https:\u002F\u002Fcourses.edx.org\u002Fcourses\u002FBerkeleyX\u002FCS188x_1\u002F1T2013\u002Finfo)\n\n- [Udacity Course | Norvig & Thrun](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-artificial-intelligence--cs271)\n\n- [TED talks on AI](http:\u002F\u002Fwww.ted.com\u002Fplaylists\u002F310\u002Ftalks_on_artificial_intelligen)\n\n\u003Ca name=\"ga\" \u002F>\n\n## Genetic Algorithms\n\n- [Genetic Algorithms Wikipedia Page](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGenetic_algorithm)\n\n- [Simple Implementation of Genetic Algorithms in Python (Part 1)](http:\u002F\u002Foutlace.com\u002Fminiga.html), [Part 2](http:\u002F\u002Foutlace.com\u002Fminiga_addendum.html)\n\n- [Genetic Algorithms vs Artificial Neural Networks](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1402370\u002Fwhen-to-use-genetic-algorithms-vs-when-to-use-neural-networks)\n\n- [Genetic Algorithms Explained in Plain English](http:\u002F\u002Fwww.ai-junkie.com\u002Fga\u002Fintro\u002Fgat1.html)\n\n- [Genetic Programming](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGenetic_programming)\n\n    - [Genetic Programming in Python (GitHub)](https:\u002F\u002Fgithub.com\u002Ftrevorstephens\u002Fgplearn)\n    \n    - [Genetic Alogorithms vs Genetic Programming (Quora)](https:\u002F\u002Fwww.quora.com\u002FWhats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F3819977\u002Fwhat-are-the-differences-between-genetic-algorithms-and-genetic-programming)\n\n\u003Ca name=\"stat\" \u002F>\n\n## Statistics\n\n- [Stat Trek Website](http:\u002F\u002Fstattrek.com\u002F) - A dedicated website to teach yourselves Statistics\n\n- [Learn Statistics Using Python](https:\u002F\u002Fgithub.com\u002Frouseguy\u002Fintro2stats) - Learn Statistics using an application-centric programming approach\n\n- [Statistics for Hackers | Slides | @jakevdp](https:\u002F\u002Fspeakerdeck.com\u002Fjakevdp\u002Fstatistics-for-hackers) - Slides by Jake VanderPlas\n\n- [Online Statistics Book](http:\u002F\u002Fonlinestatbook.com\u002F2\u002Findex.html) - An Interactive Multimedia Course for Studying Statistics\n\n- [What is a Sampling Distribution?](http:\u002F\u002Fstattrek.com\u002Fsampling\u002Fsampling-distribution.aspx)\n\n- Tutorials\n\n    - [AP Statistics Tutorial](http:\u002F\u002Fstattrek.com\u002Ftutorials\u002Fap-statistics-tutorial.aspx)\n    \n    - [Statistics and Probability Tutorial](http:\u002F\u002Fstattrek.com\u002Ftutorials\u002Fstatistics-tutorial.aspx)\n    \n    - [Matrix Algebra Tutorial](http:\u002F\u002Fstattrek.com\u002Ftutorials\u002Fmatrix-algebra-tutorial.aspx)\n    \n- [What is an Unbiased Estimator?](https:\u002F\u002Fwww.physicsforums.com\u002Fthreads\u002Fwhat-is-an-unbiased-estimator.547728\u002F)\n\n- [Goodness of Fit Explained](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGoodness_of_fit)\n\n- [What are QQ Plots?](http:\u002F\u002Fonlinestatbook.com\u002F2\u002Fadvanced_graphs\u002Fq-q_plots.html)\n\n- [OpenIntro Statistics](https:\u002F\u002Fwww.openintro.org\u002Fstat\u002Ftextbook.php?stat_book=os) - Free PDF textbook\n\n\u003Ca name=\"blogs\" \u002F>\n\n## Useful Blogs\n\n- [Edwin Chen's Blog](http:\u002F\u002Fblog.echen.me\u002F) - A blog about Math, stats, ML, crowdsourcing, data science\n\n- [The Data School Blog](http:\u002F\u002Fwww.dataschool.io\u002F) - Data science for beginners!\n\n- [ML Wave](http:\u002F\u002Fmlwave.com\u002F) - A blog for Learning Machine Learning\n\n- [Andrej Karpathy](http:\u002F\u002Fkarpathy.github.io\u002F) - A blog about Deep Learning and Data Science in general\n\n- [Colah's Blog](http:\u002F\u002Fcolah.github.io\u002F) - Awesome Neural Networks Blog\n\n- [Alex Minnaar's Blog](http:\u002F\u002Falexminnaar.com\u002F) - A blog about Machine Learning and Software Engineering\n\n- [Statistically Significant](http:\u002F\u002Fandland.github.io\u002F) - Andrew Landgraf's Data Science Blog\n\n- [Simply Statistics](http:\u002F\u002Fsimplystatistics.org\u002F) - A blog by three biostatistics professors\n\n- [Yanir Seroussi's Blog](https:\u002F\u002Fyanirseroussi.com\u002F) - A blog about Data Science and beyond\n\n- [fastML](http:\u002F\u002Ffastml.com\u002F) - Machine learning made easy\n\n- [Trevor Stephens Blog](http:\u002F\u002Ftrevorstephens.com\u002F) - Trevor Stephens Personal Page\n\n- [no free hunch | kaggle](http:\u002F\u002Fblog.kaggle.com\u002F) - The Kaggle Blog about all things Data Science\n\n- [A Quantitative Journey | outlace](http:\u002F\u002Foutlace.com\u002F) -  learning quantitative applications\n\n- [r4stats](http:\u002F\u002Fr4stats.com\u002F) - analyze the world of data science, and to help people learn to use R\n\n- [Variance Explained](http:\u002F\u002Fvarianceexplained.org\u002F) - David Robinson's Blog\n\n- [AI Junkie](http:\u002F\u002Fwww.ai-junkie.com\u002F) - a blog about Artificial Intellingence\n\n- [Deep Learning Blog by Tim Dettmers](http:\u002F\u002Ftimdettmers.com\u002F) - Making deep learning accessible\n\n- [J Alammar's Blog](http:\u002F\u002Fjalammar.github.io\u002F)- Blog posts about Machine Learning and Neural Nets\n\n- [Adam Geitgey](https:\u002F\u002Fmedium.com\u002F@ageitgey\u002Fmachine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning\n\n- [Ethen's Notebook Collection](https:\u002F\u002Fgithub.com\u002Fethen8181\u002Fmachine-learning) - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage\n\n\u003Ca name=\"quora\" \u002F>\n\n## Resources on Quora\n\n- [Most Viewed Machine Learning writers](https:\u002F\u002Fwww.quora.com\u002Ftopic\u002FMachine-Learning\u002Fwriters)\n\n- [Data Science Topic on Quora](https:\u002F\u002Fwww.quora.com\u002FData-Science)\n\n- [William Chen's Answers](https:\u002F\u002Fwww.quora.com\u002FWilliam-Chen-6\u002Fanswers)\n\n- [Michael Hochster's Answers](https:\u002F\u002Fwww.quora.com\u002FMichael-Hochster\u002Fanswers)\n\n- [Ricardo Vladimiro's Answers](https:\u002F\u002Fwww.quora.com\u002FRicardo-Vladimiro-1\u002Fanswers)\n\n- [Storytelling with Statistics](https:\u002F\u002Fdatastories.quora.com\u002F)\n\n- [Data Science FAQs on Quora](https:\u002F\u002Fwww.quora.com\u002Ftopic\u002FData-Science\u002Ffaq)\n\n- [Machine Learning FAQs on Quora](https:\u002F\u002Fwww.quora.com\u002Ftopic\u002FMachine-Learning\u002Ffaq)\n\n\u003Ca name=\"kaggle\" \u002F>\n\n## Kaggle Competitions WriteUp\n\n- [How to almost win Kaggle Competitions](https:\u002F\u002Fyanirseroussi.com\u002F2014\u002F08\u002F24\u002Fhow-to-almost-win-kaggle-competitions\u002F)\n\n- [Convolution Neural Networks for EEG detection](http:\u002F\u002Fblog.kaggle.com\u002F2015\u002F10\u002F05\u002Fgrasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj\u002F)\n\n- [Facebook Recruiting III Explained](http:\u002F\u002Falexminnaar.com\u002Ftag\u002Fkaggle-competitions.html)\n\n- [Predicting CTR with Online ML](http:\u002F\u002Fmlwave.com\u002Fpredicting-click-through-rates-with-online-machine-learning\u002F)\n\n- [How to Rank 10% in Your First Kaggle Competition](https:\u002F\u002Fdnc1994.com\u002F2016\u002F05\u002Frank-10-percent-in-first-kaggle-competition-en\u002F)\n\n\u003Ca name=\"cs\" \u002F>\n\n## Cheat Sheets\n\n- [Probability Cheat Sheet](http:\u002F\u002Fstatic1.squarespace.com\u002Fstatic\u002F54bf3241e4b0f0d81bf7ff36\u002Ft\u002F55e9494fe4b011aed10e48e5\u002F1441352015658\u002Fprobability_cheatsheet.pdf),\n[Source](http:\u002F\u002Fwww.wzchen.com\u002Fprobability-cheatsheet\u002F)\n\n- [Machine Learning Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fsoulmachine\u002Fmachine-learning-cheat-sheet)\n\n- [ML Compiled](https:\u002F\u002Fml-compiled.readthedocs.io\u002Fen\u002Flatest\u002F)\n\n\u003Ca name=\"classification\" \u002F>\n\n## Classification\n\n- [Does Balancing Classes Improve Classifier Performance?](http:\u002F\u002Fwww.win-vector.com\u002Fblog\u002F2015\u002F02\u002Fdoes-balancing-classes-improve-classifier-performance\u002F)\n\n- [What is Deviance?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F6581\u002Fwhat-is-deviance-specifically-in-cart-rpart)\n\n- [When to choose which machine learning classifier?](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F2595176\u002Fwhen-to-choose-which-machine-learning-classifier)\n\n- [What are the advantages of different classification algorithms?](https:\u002F\u002Fwww.quora.com\u002FWhat-are-the-advantages-of-different-classification-algorithms)\n\n- [ROC and AUC Explained](http:\u002F\u002Fwww.dataschool.io\u002Froc-curves-and-auc-explained\u002F) ([related video](https:\u002F\u002Fyoutu.be\u002FOAl6eAyP-yo))\n\n- [An introduction to ROC analysis](https:\u002F\u002Fccrma.stanford.edu\u002Fworkshops\u002Fmir2009\u002Freferences\u002FROCintro.pdf)\n\n- [Simple guide to confusion matrix terminology](http:\u002F\u002Fwww.dataschool.io\u002Fsimple-guide-to-confusion-matrix-terminology\u002F)\n\n\n\u003Ca name=\"linear\" \u002F>\n\n## Linear Regression\n\n- [General](#general-)\n\n    - [Assumptions of Linear Regression](http:\u002F\u002Fpareonline.net\u002Fgetvn.asp?n=2&v=8), [Stack Exchange](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F16381\u002Fwhat-is-a-complete-list-of-the-usual-assumptions-for-linear-regression)\n    \n    - [Linear Regression Comprehensive Resource](http:\u002F\u002Fpeople.duke.edu\u002F~rnau\u002Fregintro.htm)\n    \n    - [Applying and Interpreting Linear Regression](http:\u002F\u002Fwww.dataschool.io\u002Fapplying-and-interpreting-linear-regression\u002F)\n    \n    - [What does having constant variance in a linear regression model mean?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F52089\u002Fwhat-does-having-constant-variance-in-a-linear-regression-model-mean\u002F52107?stw=2#52107)\n    \n    - [Difference between linear regression on y with x and x with y](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F22718\u002Fwhat-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1)\n    \n    - [Is linear regression valid when the dependant variable is not normally distributed?](https:\u002F\u002Fwww.researchgate.net\u002Fpost\u002FIs_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed)\n- Multicollinearity and VIF\n\n    - [Dummy Variable Trap | Multicollinearity](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMulticollinearity)\n    \n    - [Dealing with multicollinearity using VIFs](https:\u002F\u002Fjonlefcheck.net\u002F2012\u002F12\u002F28\u002Fdealing-with-multicollinearity-using-variance-inflation-factors\u002F)\n\n- [Residual Analysis](#residuals-)\n\n    - [Interpreting plot.lm() in R](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F58141\u002Finterpreting-plot-lm)\n    \n    - [How to interpret a QQ plot?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F101274\u002Fhow-to-interpret-a-qq-plot?lq=1)\n    \n    - [Interpreting Residuals vs Fitted Plot](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F76226\u002Finterpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions)\n\n- [Outliers](#outliers-)\n\n    - [How should outliers be dealt with?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F175\u002Fhow-should-outliers-be-dealt-with-in-linear-regression-analysis)\n\n- [Elastic Net](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FElastic_net_regularization)\n    - [Regularization and Variable Selection via the\nElastic Net](https:\u002F\u002Fweb.stanford.edu\u002F~hastie\u002FPapers\u002Felasticnet.pdf)\n\n\u003Ca name=\"logistic\" \u002F>\n\n## Logistic Regression\n\n- [Logistic Regression Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLogistic_regression)\n\n- [Geometric Intuition of Logistic Regression](http:\u002F\u002Fflorianhartl.com\u002Flogistic-regression-geometric-intuition.html)\n\n- [Obtaining predicted categories (choosing threshold)](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F25389\u002Fobtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit)\n\n- [Residuals in logistic regression](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F1432\u002Fwhat-do-the-residuals-in-a-logistic-regression-mean)\n\n- [Difference between logit and probit models](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F20523\u002Fdifference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLogistic_regression), [Probit Model Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FProbit_model)\n\n- [Pseudo R2 for Logistic Regression](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F3559\u002Fwhich-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F8511\u002Fhow-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http:\u002F\u002Fwww.ats.ucla.edu\u002Fstat\u002Fmult_pkg\u002Ffaq\u002Fgeneral\u002FPsuedo_RSquareds.htm)\n\n- [Guide to an in-depth understanding of logistic regression](http:\u002F\u002Fwww.dataschool.io\u002Fguide-to-logistic-regression\u002F)\n\n\u003Ca name=\"validation\" \u002F>\n\n## Model Validation using Resampling\n\n- [Resampling Explained](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FResampling_(statistics))\n\n- [Partioning data set in R](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F13536537\u002Fpartitioning-data-set-in-r-based-on-multiple-classes-of-observations)\n\n- [Implementing hold-out Validaion in R](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22972854\u002Fhow-to-implement-a-hold-out-validation-in-r), [2](http:\u002F\u002Fwww.gettinggeneticsdone.com\u002F2011\u002F02\u002Fsplit-data-frame-into-testing-and.html)\n\n\u003Ca name=\"cross\" \u002F>\n\n- [Cross Validation](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCross-validation_(statistics))\n    - [How to use cross-validation in predictive modeling](http:\u002F\u002Fstuartlacy.co.uk\u002F2016\u002F02\u002F04\u002Fhow-to-correctly-use-cross-validation-in-predictive-modelling\u002F)\n    - [Training with Full dataset after CV?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F11602\u002Ftraining-with-the-full-dataset-after-cross-validation)\n    \n    - [Which CV method is best?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F103459\u002Fhow-do-i-know-which-method-of-cross-validation-is-best)\n    \n    - [Variance Estimates in k-fold CV](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F31190\u002Fvariance-estimates-in-k-fold-cross-validation)\n    \n    - [Is CV a subsitute for Validation Set?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F18856\u002Fis-cross-validation-a-proper-substitute-for-validation-set)\n    \n    - [Choice of k in k-fold CV](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F27730\u002Fchoice-of-k-in-k-fold-cross-validation)\n    \n    - [CV for ensemble learning](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F102631\u002Fk-fold-cross-validation-of-ensemble-learning)\n    \n    - [k-fold CV in R](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F22909197\u002Fcreating-folds-for-k-fold-cv-in-r-using-caret)\n    \n    - [Good Resources](http:\u002F\u002Fwww.chioka.in\u002Ftag\u002Fcross-validation\u002F)\n    \n    - Overfitting and Cross Validation\n    \n        - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http:\u002F\u002Fai.stanford.edu\u002F~ang\u002Fpapers\u002Fcv-final.pdf)\n        \n        - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](http:\u002F\u002Fwww.jmlr.org\u002Fpapers\u002Fvolume11\u002Fcawley10a\u002Fcawley10a.pdf)\n\n        - [CV for detecting and preventing Overfitting](http:\u002F\u002Fwww.autonlab.org\u002Ftutorials\u002Foverfit10.pdf)\n        \n        - [How does CV overcome the Overfitting Problem](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F9053\u002Fhow-does-cross-validation-overcome-the-overfitting-problem)\n\n\n\u003Ca name=\"boot\" \u002F>\n\n- [Bootstrapping](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBootstrapping_(statistics))\n\n    - [Why Bootstrapping Works?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F26088\u002Fexplaining-to-laypeople-why-bootstrapping-works)\n    \n    - [Good Animation](https:\u002F\u002Fwww.stat.auckland.ac.nz\u002F~wild\u002FBootAnim\u002F)\n    \n    - [Example of Bootstapping](http:\u002F\u002Fstatistics.about.com\u002Fod\u002FApplications\u002Fa\u002FExample-Of-Bootstrapping.htm)\n    \n    - [Understanding Bootstapping for Validation and Model Selection](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F14516\u002Funderstanding-bootstrapping-for-validation-and-model-selection?rq=1)\n    \n    - [Cross Validation vs Bootstrap to estimate prediction error](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F18348\u002Fdifferences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F71184\u002Fcross-validation-or-bootstrapping-to-evaluate-classification-performance)\n\n\n\u003Ca name=\"deep\" \u002F>\n\n## Deep Learning\n\n- [fast.ai - Practical Deep Learning For Coders](http:\u002F\u002Fcourse.fast.ai\u002F)\n\n- [fast.ai - Cutting Edge Deep Learning For Coders](http:\u002F\u002Fcourse.fast.ai\u002Fpart2.html)\n\n- [A curated list of awesome Deep Learning tutorials, projects and communities](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning)\n\n- **[Deep Learning Papers Reading Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap\u002Fblob\u002Fmaster\u002FREADME.md)**\n\n- [Lots of Deep Learning Resources](http:\u002F\u002Fdeeplearning4j.org\u002Fdocumentation.html)\n\n- [Interesting Deep Learning and NLP Projects (Stanford)](http:\u002F\u002Fcs224d.stanford.edu\u002Freports.html), [Website](http:\u002F\u002Fcs224d.stanford.edu\u002F)\n\n- [Core Concepts of Deep Learning](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fdeep-learning-nutshell-core-concepts\u002F)\n\n- [Understanding Natural Language with Deep Neural Networks Using Torch](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Funderstanding-natural-language-deep-neural-networks-using-torch\u002F)\n\n- [Stanford Deep Learning Tutorial](http:\u002F\u002Fufldl.stanford.edu\u002Ftutorial\u002F)\n\n- [Deep Learning FAQs on Quora](https:\u002F\u002Fwww.quora.com\u002Ftopic\u002FDeep-Learning\u002Ffaq)\n\n- [Google+ Deep Learning Page](https:\u002F\u002Fplus.google.com\u002Fcommunities\u002F112866381580457264725)\n\n- [Recent Reddit AMAs related to Deep Learning](http:\u002F\u002Fdeeplearning.net\u002F2014\u002F11\u002F22\u002Frecent-reddit-amas-about-deep-learning\u002F), [Another AMA](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FIAmA\u002Fcomments\u002F3mdk9v\u002Fwe_are_google_researchers_working_on_deep\u002F)\n\n- [Where to Learn Deep Learning?](http:\u002F\u002Fwww.kdnuggets.com\u002F2014\u002F05\u002Flearn-deep-learning-courses-tutorials-overviews.html)\n\n- [Deep Learning nvidia concepts](http:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fdeep-learning-nutshell-core-concepts\u002F)\n\n- [Introduction to Deep Learning Using Python (GitHub)](https:\u002F\u002Fgithub.com\u002Frouseguy\u002Fintro2deeplearning), [Good Introduction Slides](https:\u002F\u002Fspeakerdeck.com\u002Fbargava\u002Fintroduction-to-deep-learning)\n\n- [Video Lectures Oxford 2015](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2015_montreal\u002F)\n\n- [Deep Learning Software List](http:\u002F\u002Fdeeplearning.net\u002Fsoftware_links\u002F)\n\n- [Hacker's guide to Neural Nets](http:\u002F\u002Fkarpathy.github.io\u002Fneuralnets\u002F)\n\n- [Top arxiv Deep Learning Papers explained](http:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F10\u002Ftop-arxiv-deep-learning-papers-explained.html)\n\n- [Geoff Hinton Youtube Vidoes on Deep Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IcOMKXAw5VA)\n\n- [Awesome Deep Learning Reading List](http:\u002F\u002Fdeeplearning.net\u002Freading-list\u002F)\n\n- [Deep Learning Comprehensive Website](http:\u002F\u002Fdeeplearning.net\u002F), [Software](http:\u002F\u002Fdeeplearning.net\u002Fsoftware_links\u002F)\n\n- [deeplearning Tutorials](http:\u002F\u002Fdeeplearning4j.org\u002F)\n\n- [AWESOME! Deep Learning Tutorial](https:\u002F\u002Fwww.toptal.com\u002Fmachine-learning\u002Fan-introduction-to-deep-learning-from-perceptrons-to-deep-networks)\n\n- [Deep Learning Basics](http:\u002F\u002Falexminnaar.com\u002Fdeep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)\n\n- [Intuition Behind Backpropagation](https:\u002F\u002Fmedium.com\u002Fspidernitt\u002Fbreaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c)\n\n- [Stanford Tutorials](http:\u002F\u002Fufldl.stanford.edu\u002Ftutorial\u002Fsupervised\u002FMultiLayerNeuralNetworks\u002F)\n\n- [Train, Validation & Test in Artificial Neural Networks](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F2976452\u002Fwhats-is-the-difference-between-train-validation-and-test-set-in-neural-networ)\n\n- [Artificial Neural Networks Tutorials](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F478947\u002Fwhat-are-some-good-resources-for-learning-about-artificial-neural-networks)\n\n- [Neural Networks FAQs on Stack Overflow](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fneural-network?sort=votes&pageSize=50)\n\n- [Deep Learning Tutorials on deeplearning.net](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Findex.html)\n\n- [Neural Networks and Deep Learning Online Book](http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F)\n\n- Neural Machine Translation\n\n    - **[Machine Translation Reading List](https:\u002F\u002Fgithub.com\u002FTHUNLP-MT\u002FMT-Reading-List#machine-translation-reading-list)**\n\n    - [Introduction to Neural Machine Translation with GPUs (part 1)](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fintroduction-neural-machine-translation-with-gpus\u002F), [Part 2](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fintroduction-neural-machine-translation-gpus-part-2\u002F), [Part 3](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fintroduction-neural-machine-translation-gpus-part-3\u002F)\n    \n    - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fdeep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning\u002F)\n\n\u003Ca name=\"frame\" \u002F>\n\n- Deep Learning Frameworks\n\n    - [Torch vs. Theano](http:\u002F\u002Ffastml.com\u002Ftorch-vs-theano\u002F)\n    \n    - [dl4j vs. torch7 vs. theano](http:\u002F\u002Fdeeplearning4j.org\u002Fcompare-dl4j-torch7-pylearn.html)\n    \n    - [Deep Learning Libraries by Language](http:\u002F\u002Fwww.teglor.com\u002Fb\u002Fdeep-learning-libraries-language-cm569\u002F)\n    \n\n    - [Theano](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTheano_(software))\n    \n        - [Website](http:\u002F\u002Fdeeplearning.net\u002Fsoftware\u002Ftheano\u002F)\n        \n        - [Theano Introduction](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F09\u002Fspeeding-up-your-neural-network-with-theano-and-the-gpu\u002F)\n        \n        - [Theano Tutorial](http:\u002F\u002Foutlace.com\u002FBeginner-Tutorial-Theano\u002F)\n        \n        - [Good Theano Tutorial](http:\u002F\u002Fdeeplearning.net\u002Fsoftware\u002Ftheano\u002Ftutorial\u002F)\n        \n        - [Logistic Regression using Theano for classifying digits](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Flogreg.html#logreg)\n        \n        - [MLP using Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Fmlp.html#mlp)\n        \n        - [CNN using Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Flenet.html#lenet)\n        \n        - [RNNs using Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Frnnslu.html#rnnslu)\n        \n        - [LSTM for Sentiment Analysis in Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Flstm.html#lstm)\n        \n        - [RBM using Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Frbm.html#rbm)\n        \n        - [DBNs using Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002FDBN.html#dbn)\n        \n        - [All Codes](https:\u002F\u002Fgithub.com\u002Flisa-lab\u002FDeepLearningTutorials)\n        \n        - [Deep Learning Implementation Tutorials - Keras and Lasagne](https:\u002F\u002Fgithub.com\u002Fvict0rsch\u002Fdeep_learning\u002F)\n\n    - [Torch](http:\u002F\u002Ftorch.ch\u002F)\n    \n        - [Torch ML Tutorial](http:\u002F\u002Fcode.madbits.com\u002Fwiki\u002Fdoku.php), [Code](https:\u002F\u002Fgithub.com\u002Ftorch\u002Ftutorials)\n        \n        - [Intro to Torch](http:\u002F\u002Fml.informatik.uni-freiburg.de\u002F_media\u002Fteaching\u002Fws1415\u002Fpresentation_dl_lect3.pdf)\n        \n        - [Learning Torch GitHub Repo](https:\u002F\u002Fgithub.com\u002Fchetannaik\u002Flearning_torch)\n        \n        - [Awesome-Torch (Repository on GitHub)](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002Fawesome-torch)\n        \n        - [Machine Learning using Torch Oxford Univ](https:\u002F\u002Fwww.cs.ox.ac.uk\u002Fpeople\u002Fnando.defreitas\u002Fmachinelearning\u002F), [Code](https:\u002F\u002Fgithub.com\u002Foxford-cs-ml-2015)\n        \n        - [Torch Internals Overview](https:\u002F\u002Fapaszke.github.io\u002Ftorch-internals.html)\n        \n        - [Torch Cheatsheet](https:\u002F\u002Fgithub.com\u002Ftorch\u002Ftorch7\u002Fwiki\u002FCheatsheet)\n        \n        - [Understanding Natural Language with Deep Neural Networks Using Torch](http:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Funderstanding-natural-language-deep-neural-networks-using-torch\u002F)\n\n    - Caffe\n        - [Deep Learning for Computer Vision with Caffe and cuDNN](https:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Fdeep-learning-computer-vision-caffe-cudnn\u002F)\n\n    - TensorFlow\n        - [Website](http:\u002F\u002Ftensorflow.org\u002F)\n        \n        - [TensorFlow Examples for Beginners](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples)\n        \n        - [Stanford Tensorflow for Deep Learning Research Course](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs20si\u002Fsyllabus.html)\n        \n            - [GitHub Repo](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Ftf-stanford-tutorials)\n            \n        - [Simplified Scikit-learn Style Interface to TensorFlow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fskflow)\n        \n        - [Learning TensorFlow GitHub Repo](https:\u002F\u002Fgithub.com\u002Fchetannaik\u002Flearning_tensorflow)\n        \n        - [Benchmark TensorFlow GitHub](https:\u002F\u002Fgithub.com\u002Fsoumith\u002Fconvnet-benchmarks\u002Fissues\u002F66)\n        \n        - [Awesome TensorFlow List](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow)\n        \n        - [TensorFlow Book](https:\u002F\u002Fgithub.com\u002FBinRoot\u002FTensorFlow-Book)\n        \n        - [Android TensorFlow Machine Learning Example](https:\u002F\u002Fblog.mindorks.com\u002Fandroid-tensorflow-machine-learning-example-ff0e9b2654cc)\n        \n            - [GitHub Repo](https:\u002F\u002Fgithub.com\u002FMindorksOpenSource\u002FAndroidTensorFlowMachineLearningExample)\n        - [Creating Custom Model For Android Using TensorFlow](https:\u002F\u002Fblog.mindorks.com\u002Fcreating-custom-model-for-android-using-tensorflow-3f963d270bfb)\n            - [GitHub Repo](https:\u002F\u002Fgithub.com\u002FMindorksOpenSource\u002FAndroidTensorFlowMNISTExample)            \n\n\u003Ca name=\"feed\" \u002F>\n\n- Feed Forward Networks\n\n    - [A Quick Introduction to Neural Networks](https:\u002F\u002Fujjwalkarn.me\u002F2016\u002F08\u002F09\u002Fquick-intro-neural-networks\u002F)\n    \n    - [Implementing a Neural Network from scratch](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F09\u002Fimplementing-a-neural-network-from-scratch\u002F), [Code](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fnn-from-scratch)\n    \n    - [Speeding up your Neural Network with Theano and the gpu](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F09\u002Fspeeding-up-your-neural-network-with-theano-and-the-gpu\u002F), [Code](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fnn-theano)\n    \n    - [Basic ANN Theory](https:\u002F\u002Ftakinginitiative.wordpress.com\u002F2008\u002F04\u002F03\u002Fbasic-neural-network-tutorial-theory\u002F)\n    \n    - [Role of Bias in Neural Networks](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F2480650\u002Frole-of-bias-in-neural-networks)\n    \n    - [Choosing number of hidden layers and nodes](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F3345079\u002Festimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F10565868\u002Fmulti-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F9436209\u002Fhow-to-choose-number-of-hidden-layers-and-nodes-in-neural-network\u002F2#)\n    \n    - [Backpropagation in Matrix Form](http:\u002F\u002Fsudeepraja.github.io\u002FNeural\u002F)\n    \n    - [ANN implemented in C++ | AI Junkie](http:\u002F\u002Fwww.ai-junkie.com\u002Fann\u002Fevolved\u002Fnnt6.html)\n    \n    - [Simple Implementation](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F15395835\u002Fsimple-multi-layer-neural-network-implementation)\n    \n    - [NN for Beginners](http:\u002F\u002Fwww.codeproject.com\u002FArticles\u002F16419\u002FAI-Neural-Network-for-beginners-Part-of)\n    \n    - [Regression and Classification with NNs (Slides)](http:\u002F\u002Fwww.autonlab.org\u002Ftutorials\u002Fneural13.pdf)\n    \n    - [Another Intro](http:\u002F\u002Fwww.doc.ic.ac.uk\u002F~nd\u002Fsurprise_96\u002Fjournal\u002Fvol4\u002Fcs11\u002Freport.html)\n\n\u003Ca name=\"rnn\" \u002F>\n\n- Recurrent and LSTM Networks\n    - [awesome-rnn: list of resources (GitHub Repo)](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-rnn)\n    \n    - [Recurrent Neural Net Tutorial Part 1](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F09\u002Frecurrent-neural-networks-tutorial-part-1-introduction-to-rnns\u002F), [Part 2](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F09\u002Frecurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano\u002F), [Part 3](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F10\u002Frecurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients\u002F), [Code](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Frnn-tutorial-rnnlm\u002F)\n    \n    - [NLP RNN Representations](http:\u002F\u002Fcolah.github.io\u002Fposts\u002F2014-07-NLP-RNNs-Representations\u002F)\n    \n    - [The Unreasonable effectiveness of RNNs](http:\u002F\u002Fkarpathy.github.io\u002F2015\u002F05\u002F21\u002Frnn-effectiveness\u002F), [Torch Code](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fchar-rnn), [Python Code](https:\u002F\u002Fgist.github.com\u002Fkarpathy\u002Fd4dee566867f8291f086)\n    \n    - [Intro to RNN](http:\u002F\u002Fdeeplearning4j.org\u002Frecurrentnetwork.html), [LSTM](http:\u002F\u002Fdeeplearning4j.org\u002Flstm.html)\n    \n    - [An application of RNN](http:\u002F\u002Fhackaday.com\u002F2015\u002F10\u002F15\u002F73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next\u002F)\n    \n    - [Optimizing RNN Performance](http:\u002F\u002Fsvail.github.io\u002F)\n    \n    - [Simple RNN](http:\u002F\u002Foutlace.com\u002FSimple-Recurrent-Neural-Network\u002F)\n    \n    - [Auto-Generating Clickbait with RNN](https:\u002F\u002Flarseidnes.com\u002F2015\u002F10\u002F13\u002Fauto-generating-clickbait-with-recurrent-neural-networks\u002F)\n    \n    - [Sequence Learning using RNN (Slides)](http:\u002F\u002Fwww.slideshare.net\u002Findicods\u002Fgeneral-sequence-learning-with-recurrent-neural-networks-for-next-ml)\n    \n    - [Machine Translation using RNN (Paper)](http:\u002F\u002Femnlp2014.org\u002Fpapers\u002Fpdf\u002FEMNLP2014179.pdf)\n    \n    - [Music generation using RNNs (Keras)](https:\u002F\u002Fgithub.com\u002FMattVitelli\u002FGRUV)\n    \n    - [Using RNN to create on-the-fly dialogue (Keras)](http:\u002F\u002Fneuralniche.com\u002Fpost\u002Ftutorial\u002F)\n    \n    - Long Short Term Memory (LSTM)\n    \n        - [Understanding LSTM Networks](http:\u002F\u002Fcolah.github.io\u002Fposts\u002F2015-08-Understanding-LSTMs\u002F)\n        \n        - [LSTM explained](https:\u002F\u002Fapaszke.github.io\u002Flstm-explained.html)\n        \n        - [Beginner’s Guide to LSTM](http:\u002F\u002Fdeeplearning4j.org\u002Flstm.html)\n        \n        - [Implementing LSTM from scratch](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F10\u002Frecurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano\u002F), [Python\u002FTheano code](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Frnn-tutorial-gru-lstm)\n        \n        - [Torch Code for character-level language models using LSTM](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fchar-rnn)\n        \n        - [LSTM for Kaggle EEG Detection competition (Torch Code)](https:\u002F\u002Fgithub.com\u002Fapaszke\u002Fkaggle-grasp-and-lift)\n        \n        - [LSTM for Sentiment Analysis in Theano](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Flstm.html#lstm)\n        \n        - [Deep Learning for Visual Q&A | LSTM | CNN](http:\u002F\u002Favisingh599.github.io\u002Fdeeplearning\u002Fvisual-qa\u002F), [Code](https:\u002F\u002Fgithub.com\u002Favisingh599\u002Fvisual-qa)\n        \n        - [Computer Responds to email using LSTM | Google](http:\u002F\u002Fgoogleresearch.blogspot.in\u002F2015\u002F11\u002Fcomputer-respond-to-this-email.html)\n        \n        - [LSTM dramatically improves Google Voice Search](http:\u002F\u002Fgoogleresearch.blogspot.ch\u002F2015\u002F09\u002Fgoogle-voice-search-faster-and-more.html), [Another Article](http:\u002F\u002Fdeeplearning.net\u002F2015\u002F09\u002F30\u002Flong-short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users\u002F)\n        \n        - [Understanding Natural Language with LSTM Using Torch](http:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Funderstanding-natural-language-deep-neural-networks-using-torch\u002F)\n        \n        - [Torch code for Visual Question Answering using a CNN+LSTM model](https:\u002F\u002Fgithub.com\u002Fabhshkdz\u002Fneural-vqa)\n        \n        - [LSTM for Human Activity Recognition](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FLSTM-Human-Activity-Recognition\u002F)\n        \n    - Gated Recurrent Units (GRU)\n    \n        - [LSTM vs GRU](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F10\u002Frecurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano\u002F)\n    \n    - [Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002Fseq2seq-signal-prediction)\n\n\n\u003Ca name=\"rnn2\" \u002F>\n\n- [Recursive Neural Network (not Recurrent)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRecursive_neural_network)\n\n    - [Recursive Neural Tensor Network (RNTN)](http:\u002F\u002Fdeeplearning4j.org\u002Frecursiveneuraltensornetwork.html)\n    \n    - [word2vec, DBN, RNTN for Sentiment Analysis ](http:\u002F\u002Fdeeplearning4j.org\u002Fzh-sentiment_analysis_word2vec.html)\n\n\u003Ca name=\"rbm\" \u002F>\n\n- Restricted Boltzmann Machine\n\n    - [Beginner's Guide about RBMs](http:\u002F\u002Fdeeplearning4j.org\u002Frestrictedboltzmannmachine.html)\n    \n    - [Another Good Tutorial](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Frbm.html)\n    \n    - [Introduction to RBMs](http:\u002F\u002Fblog.echen.me\u002F2011\u002F07\u002F18\u002Fintroduction-to-restricted-boltzmann-machines\u002F)\n    \n    - [Hinton's Guide to Training RBMs](https:\u002F\u002Fwww.cs.toronto.edu\u002F~hinton\u002Fabsps\u002FguideTR.pdf)\n    \n    - [RBMs in R](https:\u002F\u002Fgithub.com\u002Fzachmayer\u002Frbm)\n    \n    - [Deep Belief Networks Tutorial](http:\u002F\u002Fdeeplearning4j.org\u002Fdeepbeliefnetwork.html)\n    \n    - [word2vec, DBN, RNTN for Sentiment Analysis ](http:\u002F\u002Fdeeplearning4j.org\u002Fzh-sentiment_analysis_word2vec.html)\n\n\u003Ca name=\"auto\" \u002F>\n\n- Autoencoders: Unsupervised (applies BackProp after setting target = input)\n\n    - [Andrew Ng Sparse Autoencoders pdf](https:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs294a\u002FsparseAutoencoder.pdf)\n    \n    - [Deep Autoencoders Tutorial](http:\u002F\u002Fdeeplearning4j.org\u002Fdeepautoencoder.html)\n    \n    - [Denoising Autoencoders](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002FdA.html), [Theano Code](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002Fcode\u002FdA.py)\n    \n    - [Stacked Denoising Autoencoders](http:\u002F\u002Fdeeplearning.net\u002Ftutorial\u002FSdA.html#sda)\n\n\n\u003Ca name=\"cnn\" \u002F>\n\n- Convolutional Neural Networks\n\n    - [An Intuitive Explanation of Convolutional Neural Networks](https:\u002F\u002Fujjwalkarn.me\u002F2016\u002F08\u002F11\u002Fintuitive-explanation-convnets\u002F)\n    \n    - [Awesome Deep Vision: List of Resources (GitHub)](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision)\n    \n    - [Intro to CNNs](http:\u002F\u002Fdeeplearning4j.org\u002Fconvolutionalnets.html)\n    \n    - [Understanding CNN for NLP](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F11\u002Funderstanding-convolutional-neural-networks-for-nlp\u002F)\n    \n    - [Stanford Notes](http:\u002F\u002Fvision.stanford.edu\u002Fteaching\u002Fcs231n\u002F), [Codes](http:\u002F\u002Fcs231n.github.io\u002F), [GitHub](https:\u002F\u002Fgithub.com\u002Fcs231n\u002Fcs231n.github.io)\n    \n    - [JavaScript Library (Browser Based) for CNNs](http:\u002F\u002Fcs.stanford.edu\u002Fpeople\u002Fkarpathy\u002Fconvnetjs\u002F)\n    \n    - [Using CNNs to detect facial keypoints](http:\u002F\u002Fdanielnouri.org\u002Fnotes\u002F2014\u002F12\u002F17\u002Fusing-convolutional-neural-nets-to-detect-facial-keypoints-tutorial\u002F)\n    \n    - [Deep learning to classify business photos at Yelp](http:\u002F\u002Fengineeringblog.yelp.com\u002F2015\u002F10\u002Fhow-we-use-deep-learning-to-classify-business-photos-at-yelp.html)\n    \n    - [Interview with Yann LeCun | Kaggle](http:\u002F\u002Fblog.kaggle.com\u002F2014\u002F12\u002F22\u002Fconvolutional-nets-and-cifar-10-an-interview-with-yan-lecun\u002F)\n    \n    - [Visualising and Understanding CNNs](https:\u002F\u002Fwww.cs.nyu.edu\u002F~fergus\u002Fpapers\u002FzeilerECCV2014.pdf)\n\n\u003Ca name=\"nrl\" \u002F>\n\n- Network Representation Learning\n\n    - [Awesome Graph Embedding](https:\u002F\u002Fgithub.com\u002Fbenedekrozemberczki\u002Fawesome-graph-embedding)\n    \n    - [Awesome Network Embedding](https:\u002F\u002Fgithub.com\u002Fchihming\u002Fawesome-network-embedding)\n    \n    - [Network Representation Learning Papers](https:\u002F\u002Fgithub.com\u002Fthunlp)\n    \n    - [Knowledge Representation Learning Papers](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FKRLPapers)\n    \n    - [Graph Based Deep Learning Literature](https:\u002F\u002Fgithub.com\u002Fnaganandy\u002Fgraph-based-deep-learning-literature)\n\n\u003Ca name=\"nlp\" \u002F>\n\n## Natural Language Processing\n\n- [A curated list of speech and natural language processing resources](https:\u002F\u002Fgithub.com\u002Fedobashira\u002Fspeech-language-processing)\n\n- [Understanding Natural Language with Deep Neural Networks Using Torch](http:\u002F\u002Fdevblogs.nvidia.com\u002Fparallelforall\u002Funderstanding-natural-language-deep-neural-networks-using-torch\u002F)\n\n- [tf-idf explained](http:\u002F\u002Fmichaelerasm.us\u002Fpost\u002Ftf-idf-in-10-minutes\u002F)\n\n- [Interesting Deep Learning NLP Projects Stanford](http:\u002F\u002Fcs224d.stanford.edu\u002Freports.html), [Website](http:\u002F\u002Fcs224d.stanford.edu\u002F)\n\n- [The Stanford NLP Group](https:\u002F\u002Fnlp.stanford.edu\u002F)\n\n- [NLP from Scratch | Google Paper](https:\u002F\u002Fstatic.googleusercontent.com\u002Fmedia\u002Fresearch.google.com\u002Fen\u002Fus\u002Fpubs\u002Farchive\u002F35671.pdf)\n\n- [Graph Based Semi Supervised Learning for NLP](http:\u002F\u002Fgraph-ssl.wdfiles.com\u002Flocal--files\u002Fblog%3A_start\u002Fgraph_ssl_acl12_tutorial_slides_final.pdf)\n\n- [Bag of Words](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBag-of-words_model)\n\n    - [Classification text with Bag of Words](http:\u002F\u002Ffastml.com\u002Fclassifying-text-with-bag-of-words-a-tutorial\u002F)\n    \n\u003Ca name=\"topic\" \u002F>\n\n- Topic Modeling\n    - [Topic Modeling Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTopic_model) \n    - [**Probabilistic Topic Models Princeton PDF**](http:\u002F\u002Fwww.cs.columbia.edu\u002F~blei\u002Fpapers\u002FBlei2012.pdf)\n\n    - [LDA Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLatent_Dirichlet_allocation), [LSA Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLatent_semantic_analysis), [Probabilistic LSA Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FProbabilistic_latent_semantic_analysis)\n    \n    - [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https:\u002F\u002Fwww.quora.com\u002FWhat-is-a-good-explanation-of-Latent-Dirichlet-Allocation)\n    \n    - [**Introduction to LDA**](http:\u002F\u002Fblog.echen.me\u002F2011\u002F08\u002F22\u002Fintroduction-to-latent-dirichlet-allocation\u002F), [Another good explanation](http:\u002F\u002Fconfusedlanguagetech.blogspot.in\u002F2012\u002F07\u002Fjordan-boyd-graber-and-philip-resnik.html)\n    \n    - [The LDA Buffet - Intuitive Explanation](http:\u002F\u002Fwww.matthewjockers.net\u002F2011\u002F09\u002F29\u002Fthe-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors\u002F)\n    \n    - [Your Guide to Latent Dirichlet Allocation (LDA)](https:\u002F\u002Fmedium.com\u002F@lettier\u002Fhow-does-lda-work-ill-explain-using-emoji-108abf40fa7d)\n    \n    - [Difference between LSI and LDA](https:\u002F\u002Fwww.quora.com\u002FWhats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA)\n    \n    - [Original LDA Paper](https:\u002F\u002Fwww.cs.princeton.edu\u002F~blei\u002Fpapers\u002FBleiNgJordan2003.pdf)\n    \n    - [alpha and beta in LDA](http:\u002F\u002Fdatascience.stackexchange.com\u002Fquestions\u002F199\u002Fwhat-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a)\n    \n    - [Intuitive explanation of the Dirichlet distribution](https:\u002F\u002Fwww.quora.com\u002FWhat-is-an-intuitive-explanation-of-the-Dirichlet-distribution)\n    - [topicmodels: An R Package for Fitting Topic Models](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Ftopicmodels\u002Fvignettes\u002Ftopicmodels.pdf)\n\n    - [Topic modeling made just simple enough](https:\u002F\u002Ftedunderwood.com\u002F2012\u002F04\u002F07\u002Ftopic-modeling-made-just-simple-enough\u002F)\n    \n    - [Online LDA](http:\u002F\u002Falexminnaar.com\u002Fonline-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http:\u002F\u002Falexminnaar.com\u002Fdistributed-online-latent-dirichlet-allocation-with-apache-spark.html)\n    \n    - [LDA in Scala](http:\u002F\u002Falexminnaar.com\u002Flatent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http:\u002F\u002Falexminnaar.com\u002Flatent-dirichlet-allocation-in-scala-part-ii-the-code.html)\n    \n    - [Segmentation of Twitter Timelines via Topic Modeling](https:\u002F\u002Falexisperrier.com\u002Fnlp\u002F2015\u002F09\u002F16\u002Fsegmentation_twitter_timelines_lda_vs_lsa.html)\n    \n    - [Topic Modeling of Twitter Followers](http:\u002F\u002Falexperrier.github.io\u002Fjekyll\u002Fupdate\u002F2015\u002F09\u002F04\u002Ftopic-modeling-of-twitter-followers.html)\n\n    - [Multilingual Latent Dirichlet Allocation (LDA)](https:\u002F\u002Fgithub.com\u002FArtificiAI\u002FMultilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https:\u002F\u002Fgithub.com\u002FArtificiAI\u002FMultilingual-Latent-Dirichlet-Allocation-LDA\u002Fblob\u002Fmaster\u002FMultilingual-LDA-Pipeline-Tutorial.ipynb))\n\n    - [Deep Belief Nets for Topic Modeling](https:\u002F\u002Fgithub.com\u002Flarsmaaloee\u002Fdeep-belief-nets-for-topic-modeling)\n    - [Gaussian LDA for Topic Models with Word Embeddings](http:\u002F\u002Fwww.cs.cmu.edu\u002F~rajarshd\u002Fpapers\u002Facl2015.pdf)\n    - Python\n        - [Series of lecture notes for probabilistic topic models written in ipython notebook](https:\u002F\u002Fgithub.com\u002Farongdari\u002Ftopic-model-lecture-note)\n        - [Implementation of various topic models in Python](https:\u002F\u002Fgithub.com\u002Farongdari\u002Fpython-topic-model)\n           \n\u003Ca name=\"word2vec\" \u002F>\n\n- word2vec\n\n    - [Google word2vec](https:\u002F\u002Fcode.google.com\u002Farchive\u002Fp\u002Fword2vec)\n    \n    - [Bag of Words Model Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBag-of-words_model)\n    \n    - [word2vec Tutorial](https:\u002F\u002Frare-technologies.com\u002Fword2vec-tutorial\u002F)\n    \n    - [A closer look at Skip Gram Modeling](http:\u002F\u002Fhomepages.inf.ed.ac.uk\u002Fballison\u002Fpdf\u002Flrec_skipgrams.pdf)\n    \n    - [Skip Gram Model Tutorial](http:\u002F\u002Falexminnaar.com\u002Fword2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http:\u002F\u002Falexminnaar.com\u002Fword2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html)\n    \n    - [Word Vectors Kaggle Tutorial Python](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fword2vec-nlp-tutorial\u002Fdetails\u002Fpart-2-word-vectors), [Part 2](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fword2vec-nlp-tutorial\u002Fdetails\u002Fpart-3-more-fun-with-word-vectors)\n    \n    - [Making sense of word2vec](http:\u002F\u002Frare-technologies.com\u002Fmaking-sense-of-word2vec\u002F)\n    \n    - [word2vec explained on deeplearning4j](http:\u002F\u002Fdeeplearning4j.org\u002Fword2vec.html)\n    \n    - [Quora word2vec](https:\u002F\u002Fwww.quora.com\u002FHow-does-word2vec-work)\n    \n    - [Other Quora Resources](https:\u002F\u002Fwww.quora.com\u002FWhat-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms), [2](https:\u002F\u002Fwww.quora.com\u002FWhat-is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https:\u002F\u002Fwww.quora.com\u002FIs-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why)\n    \n    - [word2vec, DBN, RNTN for Sentiment Analysis ](http:\u002F\u002Fdeeplearning4j.org\u002Fzh-sentiment_analysis_word2vec.html)\n\n- Text Clustering\n\n    - [How string clustering works](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F8196371\u002Fhow-clustering-works-especially-string-clustering)\n    \n    - [Levenshtein distance for measuring the difference between two sequences](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FLevenshtein_distance)\n    \n    - [Text clustering with Levenshtein distances](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F21511801\u002Ftext-clustering-with-levenshtein-distances)\n\n- Text Classification\n\n    - [Classification Text with Bag of Words](http:\u002F\u002Ffastml.com\u002Fclassifying-text-with-bag-of-words-a-tutorial\u002F)\n\n- Named Entity Recognitation \n    \n     - [Stanford Named Entity Recognizer (NER)](https:\u002F\u002Fnlp.stanford.edu\u002Fsoftware\u002FCRF-NER.shtml)\n\n     - [Named Entity Recognition: Applications and Use Cases- Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fnamed-entity-recognition-applications-and-use-cases-acdbf57d595e)\n\t\n- [Language learning with NLP and reinforcement learning](http:\u002F\u002Fblog.dennybritz.com\u002F2015\u002F09\u002F11\u002Freimagining-language-learning-with-nlp-and-reinforcement-learning\u002F)\n\n- [Kaggle Tutorial Bag of Words and Word vectors](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fword2vec-nlp-tutorial\u002Fdetails\u002Fpart-1-for-beginners-bag-of-words), [Part 2](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fword2vec-nlp-tutorial\u002Fdetails\u002Fpart-2-word-vectors), [Part 3](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fword2vec-nlp-tutorial\u002Fdetails\u002Fpart-3-more-fun-with-word-vectors)\n\n- [What would Shakespeare say (NLP Tutorial)](https:\u002F\u002Fgigadom.wordpress.com\u002F2015\u002F10\u002F02\u002Fnatural-language-processing-what-would-shakespeare-say\u002F)\n\n- [A closer look at Skip Gram Modeling](http:\u002F\u002Fhomepages.inf.ed.ac.uk\u002Fballison\u002Fpdf\u002Flrec_skipgrams.pdf)\n\n\u003Ca name=\"vision\" \u002F>\n\n## Computer Vision\n- [Awesome computer vision (github)](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision)\n\n- [Awesome deep vision (github)](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision)\n\n\n\u003Ca name=\"svm\" \u002F>\n\n## Support Vector Machine\n\n- [Highest Voted Questions about SVMs on Cross Validated](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Fsvm)\n\n- [Help me Understand SVMs!](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F3947\u002Fhelp-me-understand-support-vector-machines)\n\n- [SVM in Layman's terms](https:\u002F\u002Fwww.quora.com\u002FWhat-does-support-vector-machine-SVM-mean-in-laymans-terms)\n\n- [How does SVM Work | Comparisons](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F23391\u002Fhow-does-a-support-vector-machine-svm-work)\n\n- [A tutorial on SVMs](http:\u002F\u002Falex.smola.org\u002Fpapers\u002F2003\u002FSmoSch03b.pdf)\n\n- [Practical Guide to SVC](http:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~cjlin\u002Fpapers\u002Fguide\u002Fguide.pdf), [Slides](http:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~cjlin\u002Ftalks\u002Ffreiburg.pdf)\n\n- [Introductory Overview of SVMs](http:\u002F\u002Fwww.statsoft.com\u002FTextbook\u002FSupport-Vector-Machines)\n\n- Comparisons\n\n    - [SVMs > ANNs](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F6699222\u002Fsupport-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F11632516\u002Fwhat-are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](http:\u002F\u002Fwww.svms.org\u002Fanns.html)\n    \n    - [Trees > SVMs](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F57438\u002Fwhy-is-svm-not-so-good-as-decision-tree-on-the-same-data)\n    \n    - [Kernel Logistic Regression vs SVM](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F43996\u002Fkernel-logistic-regression-vs-svm)\n    \n    - [Logistic Regression vs SVM](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F58684\u002Fregularized-logistic-regression-and-support-vector-machine), [2](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F95340\u002Fsvm-v-s-logistic-regression), [3](https:\u002F\u002Fwww.quora.com\u002FSupport-Vector-Machines\u002FWhat-is-the-difference-between-Linear-SVMs-and-Logistic-Regression)\n    \n- [Optimization Algorithms in Support Vector Machines](http:\u002F\u002Fpages.cs.wisc.edu\u002F~swright\u002Ftalks\u002Fsjw-complearning.pdf)\n\n- [Variable Importance from SVM](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F2179\u002Fvariable-importance-from-svm)\n\n- Software\n\n    - [LIBSVM](https:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~cjlin\u002Flibsvm\u002F)\n    \n    - [Intro to SVM in R](http:\u002F\u002Fcbio.ensmp.fr\u002F~jvert\u002Fsvn\u002Ftutorials\u002Fpractical\u002Fsvmbasic\u002Fsvmbasic_notes.pdf)\n    \n- Kernels\n    - [What are Kernels in ML and SVM?](https:\u002F\u002Fwww.quora.com\u002FWhat-are-Kernels-in-Machine-Learning-and-SVM)\n    \n    - [Intuition Behind Gaussian Kernel in SVMs?](https:\u002F\u002Fwww.quora.com\u002FSupport-Vector-Machines\u002FWhat-is-the-intuition-behind-Gaussian-kernel-in-SVM)\n    \n- Probabilities post SVM\n\n    - [Platt's Probabilistic Outputs for SVM](http:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~htlin\u002Fpaper\u002Fdoc\u002Fplattprob.pdf)\n    \n    - [Platt Calibration Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPlatt_scaling)\n    \n    - [Why use Platts Scaling](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F5196\u002Fwhy-use-platts-scaling)\n    \n    - [Classifier Classification with Platt's Scaling](http:\u002F\u002Ffastml.com\u002Fclassifier-calibration-with-platts-scaling-and-isotonic-regression\u002F)\n\n\n\u003Ca name=\"rl\" \u002F>\n\n## Reinforcement Learning\n\n- [Awesome Reinforcement Learning (GitHub)](https:\u002F\u002Fgithub.com\u002Faikorea\u002Fawesome-rl)\n\n- [RL Tutorial Part 1](http:\u002F\u002Foutlace.com\u002FReinforcement-Learning-Part-1\u002F), [Part 2](http:\u002F\u002Foutlace.com\u002FReinforcement-Learning-Part-2\u002F)\n\n\u003Ca name=\"dt\" \u002F>\n\n## Decision Trees\n\n- [Wikipedia Page - Lots of Good Info](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FDecision_tree_learning)\n\n- [FAQs about Decision Trees](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Fcart)\n\n- [Brief Tour of Trees and Forests](https:\u002F\u002Fstatistical-research.com\u002Findex.php\u002F2013\u002F04\u002F29\u002Fa-brief-tour-of-the-trees-and-forests\u002F)\n\n- [Tree Based Models in R](http:\u002F\u002Fwww.statmethods.net\u002Fadvstats\u002Fcart.html)\n\n- [How Decision Trees work?](http:\u002F\u002Fwww.aihorizon.com\u002Fessays\u002Fgeneralai\u002Fdecision_trees.htm)\n\n- [Weak side of Decision Trees](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F1292\u002Fwhat-is-the-weak-side-of-decision-trees)\n\n- [Thorough Explanation and different algorithms](http:\u002F\u002Fwww.ise.bgu.ac.il\u002Ffaculty\u002Fliorr\u002Fhbchap9.pdf)\n\n- [What is entropy and information gain in the context of building decision trees?](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1859554\u002Fwhat-is-entropy-and-information-gain)\n\n- [Slides Related to Decision Trees](http:\u002F\u002Fwww.slideshare.net\u002Fpierluca.lanzi\u002Fmachine-learning-and-data-mining-11-decision-trees)\n\n- [How do decision tree learning algorithms deal with missing values?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F96025\u002Fhow-do-decision-tree-learning-algorithms-deal-with-missing-values-under-the-hoo)\n\n- [Using Surrogates to Improve Datasets with Missing Values](https:\u002F\u002Fwww.salford-systems.com\u002Fvideos\u002Ftutorials\u002Ftips-and-tricks\u002Fusing-surrogates-to-improve-datasets-with-missing-values)\n\n- [Good Article](https:\u002F\u002Fwww.mindtools.com\u002Fdectree.html)\n\n- [Are decision trees almost always binary trees?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F12187\u002Fare-decision-trees-almost-always-binary-trees)\n\n- [Pruning Decision Trees](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPruning_(decision_trees)), [Grafting of Decision Trees](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGrafting_(decision_trees))\n\n- [What is Deviance in context of Decision Trees?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F6581\u002Fwhat-is-deviance-specifically-in-cart-rpart)\n\n- [Discover structure behind data with decision trees](http:\u002F\u002Fvooban.com\u002Fen\u002Ftips-articles-geek-stuff\u002Fdiscover-structure-behind-data-with-decision-trees\u002F) - Grow and plot a decision tree to automatically figure out hidden rules in your data\n\n- Comparison of Different Algorithms\n\n    - [CART vs CTREE](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F12140\u002Fconditional-inference-trees-vs-traditional-decision-trees)\n    \n    - [Comparison of complexity or performance](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F9979461\u002Fdifferent-decision-tree-algorithms-with-comparison-of-complexity-or-performance)\n    \n    - [CHAID vs CART](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F61230\u002Fchaid-vs-crt-or-cart) , [CART vs CHAID](http:\u002F\u002Fwww.bzst.com\u002F2006\u002F10\u002Fclassification-trees-cart-vs-chaid.html)\n    \n    - [Good Article on comparison](http:\u002F\u002Fwww.ftpress.com\u002Farticles\u002Farticle.aspx?p=2248639&seqNum=11)\n    \n- CART\n\n    - [Recursive Partitioning Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FRecursive_partitioning)\n    \n    - [CART Explained](http:\u002F\u002Fdocuments.software.dell.com\u002FStatistics\u002FTextbook\u002FClassification-and-Regression-Trees)\n    \n    - [How to measure\u002Frank “variable importance” when using CART?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F6478\u002Fhow-to-measure-rank-variable-importance-when-using-cart-specifically-using)\n    \n    - [Pruning a Tree in R](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F15318409\u002Fhow-to-prune-a-tree-in-r)\n    \n    - [Does rpart use multivariate splits by default?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F4356\u002Fdoes-rpart-use-multivariate-splits-by-default)\n    \n    - [FAQs about Recursive Partitioning](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Frpart)\n    \n- CTREE\n\n    - [party package in R](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fparty\u002Fparty.pdf)\n    \n    - [Show volumne in each node using ctree in R](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F13772715\u002Fshow-volume-in-each-node-using-ctree-plot-in-r)\n    \n    - [How to extract tree structure from ctree function?](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F8675664\u002Fhow-to-extract-tree-structure-from-ctree-function)\n    \n- CHAID\n\n    - [Wikipedia Artice on CHAID](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCHAID)\n    \n    - [Basic Introduction to CHAID](https:\u002F\u002Fsmartdrill.com\u002FIntroduction-to-CHAID.html)\n    \n    - [Good Tutorial on CHAID](http:\u002F\u002Fwww.statsoft.com\u002FTextbook\u002FCHAID-Analysis)\n    \n- MARS\n\n    - [Wikipedia Article on MARS](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMultivariate_adaptive_regression_splines)\n    \n- Probabilistic Decision Trees\n\n    - [Bayesian Learning in Probabilistic Decision Trees](http:\u002F\u002Fwww.stats.org.uk\u002Fbayesian\u002FJordan.pdf)\n    \n    - [Probabilistic Trees Research Paper](http:\u002F\u002Fpeople.stern.nyu.edu\u002Fadamodar\u002Fpdfiles\u002Fpapers\u002Fprobabilistic.pdf)\n\n\u003Ca name=\"rf\" \u002F>\n\n## Random Forest \u002F Bagging\n\n- [Awesome Random Forest (GitHub)**](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-random-forest)\n\n- [How to tune RF parameters in practice?](https:\u002F\u002Fwww.kaggle.com\u002Fforums\u002Ff\u002F15\u002Fkaggle-forum\u002Ft\u002F4092\u002Fhow-to-tune-rf-parameters-in-practice)\n\n- [Measures of variable importance in random forests](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F12605\u002Fmeasures-of-variable-importance-in-random-forests)\n\n- [Compare R-squared from two different Random Forest models](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F13869\u002Fcompare-r-squared-from-two-different-random-forest-models)\n\n- [OOB Estimate Explained | RF vs LDA](https:\u002F\u002Fstat.ethz.ch\u002Feducation\u002Fsemesters\u002Fss2012\u002Fams\u002Fslides\u002Fv10.2.pdf)\n\n- [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](https:\u002F\u002Fwww.jstatsoft.org\u002Findex.php\u002Fjss\u002Farticle\u002Fview\u002Fv050i11)\n\n- [Why doesn't Random Forest handle missing values in predictors?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F98953\u002Fwhy-doesnt-random-forest-handle-missing-values-in-predictors)\n\n- [How to build random forests in R with missing (NA) values?](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F8370455\u002Fhow-to-build-random-forests-in-r-with-missing-na-values)\n\n- [FAQs about Random Forest](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Frandom-forest), [More FAQs](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Frandom-forest)\n\n- [Obtaining knowledge from a random forest](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F21152\u002Fobtaining-knowledge-from-a-random-forest)\n\n- [Some Questions for R implementation](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F20537186\u002Fgetting-predictions-after-rfimpute), [2](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F81609\u002Fwhether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F17059432\u002Frandom-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact)\n\n\u003Ca name=\"gbm\" \u002F>\n\n## Boosting\n\n- [Boosting for Better Predictions](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Fboosting-algorithms-for-better-predictions)\n\n- [Boosting Wikipedia Page](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBoosting_(machine_learning))\n\n- [Introduction to Boosted Trees | Tianqi Chen](https:\u002F\u002Fhomes.cs.washington.edu\u002F~tqchen\u002Fpdf\u002FBoostedTree.pdf)\n\n- Gradient Boosting Machine\n\n    - [Gradiet Boosting Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FGradient_boosting)\n    \n    - [Guidelines for GBM parameters in R](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F25748\u002Fwhat-are-some-useful-guidelines-for-gbm-parameters), [Strategy to set parameters](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F35984\u002Fstrategy-to-set-the-gbm-parameters)\n    \n    - [Meaning of Interaction Depth](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F16501\u002Fwhat-does-interaction-depth-mean-in-gbm), [2](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F16501\u002Fwhat-does-interaction-depth-mean-in-gbm)\n    \n    - [Role of n.minobsinnode parameter of GBM in R](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F30645\u002Frole-of-n-minobsinnode-parameter-of-gbm-in-r)\n    \n    - [GBM in R](http:\u002F\u002Fwww.slideshare.net\u002Fmark_landry\u002Fgbm-package-in-r)\n    \n    - [FAQs about GBM](http:\u002F\u002Fstats.stackexchange.com\u002Ftags\u002Fgbm\u002Fhot)\n    \n    - [GBM vs xgboost](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fhiggs-boson\u002Fforums\u002Ft\u002F9497\u002Fr-s-gbm-vs-python-s-xgboost)\n\n- xgboost\n\n    - [xgboost tuning kaggle](https:\u002F\u002Fwww.kaggle.com\u002Fkhozzy\u002Frossmann-store-sales\u002Fxgboost-parameter-tuning-template\u002Flog)\n    \n    - [xgboost vs gbm](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fotto-group-product-classification-challenge\u002Fforums\u002Ft\u002F13012\u002Fquestion-to-experienced-kagglers-and-anyone-who-wants-to-take-a-shot\u002F68296#post68296)\n    \n    - [xgboost survey](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fhiggs-boson\u002Fforums\u002Ft\u002F10335\u002Fxgboost-post-competition-survey)\n    \n    - [Practical XGBoost in Python online course (free)](http:\u002F\u002Feducation.parrotprediction.teachable.com\u002Fcourses\u002Fpractical-xgboost-in-python)\n    \n- AdaBoost\n\n    - [AdaBoost Wiki](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAdaBoost), [Python Code](https:\u002F\u002Fgist.github.com\u002Ftristanwietsma\u002F5486024)\n    \n    - [AdaBoost Sparse Input Support](http:\u002F\u002Fhamzehal.blogspot.com\u002F2014\u002F06\u002Fadaboost-sparse-input-support.html)\n    \n    - [adaBag R package](https:\u002F\u002Fcran.r-project.org\u002Fweb\u002Fpackages\u002Fadabag\u002Fadabag.pdf)\n    \n    - [Tutorial](http:\u002F\u002Fmath.mit.edu\u002F~rothvoss\u002F18.304.3PM\u002FPresentations\u002F1-Eric-Boosting304FinalRpdf.pdf)\n\n- CatBoost\n\n    - [CatBoost Documentation](https:\u002F\u002Fcatboost.ai\u002Fdocs\u002F)\n\n    - [Benchmarks](https:\u002F\u002Fcatboost.ai\u002F#benchmark)\n\n    - [Tutorial](https:\u002F\u002Fgithub.com\u002Fcatboost\u002Ftutorials)\n\n    - [GitHub Project](https:\u002F\u002Fgithub.com\u002Fcatboost)\n\n    - [CatBoost vs. Light GBM vs. XGBoost](https:\u002F\u002Ftowardsdatascience.com\u002Fcatboost-vs-light-gbm-vs-xgboost-5f93620723db)\n\n\u003Ca name=\"ensem\" \u002F>\n\n## Ensembles\n\n- [Wikipedia Article on Ensemble Learning](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FEnsemble_learning)\n\n- [Kaggle Ensembling Guide](http:\u002F\u002Fmlwave.com\u002Fkaggle-ensembling-guide\u002F)\n\n- [The Power of Simple Ensembles](http:\u002F\u002Fwww.overkillanalytics.net\u002Fmore-is-always-better-the-power-of-simple-ensembles\u002F)\n\n- [Ensemble Learning Intro](http:\u002F\u002Fmachine-learning.martinsewell.com\u002Fensembles\u002F)\n\n- [Ensemble Learning Paper](http:\u002F\u002Fcs.nju.edu.cn\u002Fzhouzh\u002Fzhouzh.files\u002Fpublication\u002FspringerEBR09.pdf)\n\n- [Ensembling models with R](http:\u002F\u002Famunategui.github.io\u002Fblending-models\u002F), [Ensembling Regression Models in R](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F26790\u002Fensembling-regression-models), [Intro to Ensembles in R](http:\u002F\u002Fwww.vikparuchuri.com\u002Fblog\u002Fintro-to-ensemble-learning-in-r\u002F)\n\n- [Ensembling Models with caret](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F27361\u002Fstacking-ensembling-models-with-caret)\n\n- [Bagging vs Boosting vs Stacking](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F18891\u002Fbagging-boosting-and-stacking-in-machine-learning)\n\n- [Good Resources | Kaggle Africa Soil Property Prediction](https:\u002F\u002Fwww.kaggle.com\u002Fc\u002Fafsis-soil-properties\u002Fforums\u002Ft\u002F10391\u002Fbest-ensemble-references)\n\n- [Boosting vs Bagging](http:\u002F\u002Fwww.chioka.in\u002Fwhich-is-better-boosting-or-bagging\u002F)\n\n- [Resources for learning how to implement ensemble methods](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F32703\u002Fresources-for-learning-how-to-implement-ensemble-methods)\n\n- [How are classifications merged in an ensemble classifier?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F21502\u002Fhow-are-classifications-merged-in-an-ensemble-classifier)\n\n\u003Ca name=\"stack\" \u002F>\n\n## Stacking Models\n\n- [Stacking, Blending and Stacked Generalization](http:\u002F\u002Fwww.chioka.in\u002Fstacking-blending-and-stacked-generalization\u002F)\n\n- [Stacked Generalization (Stacking)](http:\u002F\u002Fmachine-learning.martinsewell.com\u002Fensembles\u002Fstacking\u002F)\n\n- [Stacked Generalization: when does it work?](http:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F97-2\u002F011.pdf)\n\n- [Stacked Generalization Paper](http:\u002F\u002Fciteseerx.ist.psu.edu\u002Fviewdoc\u002Fdownload?doi=10.1.1.56.1533&rep=rep1&type=pdf)\n\n\u003Ca name=\"vc\" \u002F>\n\n## Vapnik–Chervonenkis Dimension\n\n- [Wikipedia article on VC Dimension](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FVC_dimension)\n\n- [Intuitive Explanantion of VC Dimension](https:\u002F\u002Fwww.quora.com\u002FExplain-VC-dimension-and-shattering-in-lucid-Way)\n\n- [Video explaining VC Dimension](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=puDzy2XmR5c)\n\n- [Introduction to VC Dimension](http:\u002F\u002Fwww.svms.org\u002Fvc-dimension\u002F)\n\n- [FAQs about VC Dimension](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002Ftagged\u002Fvc-dimension)\n\n- [Do ensemble techniques increase VC-dimension?](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F78076\u002Fdo-ensemble-techniques-increase-vc-dimension)\n\n\n\u003Ca name=\"bayes\" \u002F>\n\n## Bayesian Machine Learning\n\n- [Bayesian Methods for Hackers (using pyMC)](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers)\n\n- [Should all Machine Learning be Bayesian?](http:\u002F\u002Fvideolectures.net\u002Fbark08_ghahramani_samlbb\u002F)\n\n- [Tutorial on Bayesian Optimisation for Machine Learning](http:\u002F\u002Fwww.iro.umontreal.ca\u002F~bengioy\u002Fcifar\u002FNCAP2014-summerschool\u002Fslides\u002FRyan_adams_140814_bayesopt_ncap.pdf)\n\n- [Bayesian Reasoning and Deep Learning](http:\u002F\u002Fblog.shakirm.com\u002F2015\u002F10\u002Fbayesian-reasoning-and-deep-learning\u002F), [Slides](http:\u002F\u002Fblog.shakirm.com\u002Fwp-content\u002Fuploads\u002F2015\u002F10\u002FBayes_Deep.pdf)\n\n- [Bayesian Statistics Made Simple](http:\u002F\u002Fgreenteapress.com\u002Fwp\u002Fthink-bayes\u002F)\n\n- [Kalman & Bayesian Filters in Python](https:\u002F\u002Fgithub.com\u002Frlabbe\u002FKalman-and-Bayesian-Filters-in-Python)\n\n- [Markov Chain Wikipedia Page](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMarkov_chain)\n\n\n\u003Ca name=\"semi\" \u002F>\n\n## Semi Supervised Learning\n\n- [Wikipedia article on Semi Supervised Learning](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSemi-supervised_learning)\n\n- [Tutorial on Semi Supervised Learning](http:\u002F\u002Fpages.cs.wisc.edu\u002F~jerryzhu\u002Fpub\u002Fsslicml07.pdf)\n\n- [Graph Based Semi Supervised Learning for NLP](http:\u002F\u002Fgraph-ssl.wdfiles.com\u002Flocal--files\u002Fblog%3A_start\u002Fgraph_ssl_acl12_tutorial_slides_final.pdf)\n\n- [Taxonomy](http:\u002F\u002Fis.tuebingen.mpg.de\u002Ffileadmin\u002Fuser_upload\u002Ffiles\u002Fpublications\u002Ftaxo_[0].pdf)\n\n- [Video Tutorial Weka](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sWxcIjZFGNM)\n\n- [Unsupervised, Supervised and Semi Supervised learning](http:\u002F\u002Fstats.stackexchange.com\u002Fquestions\u002F517\u002Funsupervised-supervised-and-semi-supervised-learning)\n\n- [Research Papers 1](http:\u002F\u002Fmlg.eng.cam.ac.uk\u002Fzoubin\u002Fpapers\u002Fzglactive.pdf), [2](http:\u002F\u002Fmlg.eng.cam.ac.uk\u002Fzoubin\u002Fpapers\u002Fzgl.pdf), [3](http:\u002F\u002Ficml.cc\u002F2012\u002Fpapers\u002F616.pdf)\n\n\n\u003Ca name=\"opt\" \u002F>\n\n## Optimization\n\n- [Mean Variance Portfolio Optimization with R and Quadratic Programming](http:\u002F\u002Fwww.wdiam.com\u002F2012\u002F06\u002F10\u002Fmean-variance-portfolio-optimization-with-r-and-quadratic-programming\u002F?utm_content=buffer04c12&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n\n- [Algorithms for Sparse Optimization and Machine Learning](http:\u002F\u002Fwww.ima.umn.edu\u002F2011-2012\u002FW3.26-30.12\u002Factivities\u002FWright-Steve\u002Fsjw-ima12)\n\n- [Optimization Algorithms in Machine Learning](http:\u002F\u002Fpages.cs.wisc.edu\u002F~swright\u002Fnips2010\u002Fsjw-nips10.pdf), [Video Lecture](http:\u002F\u002Fvideolectures.net\u002Fnips2010_wright_oaml\u002F)\n\n- [Optimization Algorithms for Data Analysis](http:\u002F\u002Fwww.birs.ca\u002Fworkshops\u002F2011\u002F11w2035\u002Ffiles\u002FWright.pdf)\n\n- [Video Lectures on Optimization](http:\u002F\u002Fvideolectures.net\u002Fstephen_j_wright\u002F)\n\n- [Opt","该项目是一个机器学习和深度学习的教程、文章和其他资源的精选列表。它涵盖了从基础到高级的各种主题，包括但不限于分类、回归、深度神经网络、自然语言处理等，并且提供了丰富的学习材料链接，如在线课程、博客文章以及研究论文。技术特点在于其全面性和实用性，适合初学者入门以及专业人士深化理解。无论是对学术研究还是工业应用感兴趣的开发者，都能在此找到合适的学习资料。",2,"2026-06-11 03:23:38","top_topic"]