├── LICENSE ├── README.md └── contributing.md /LICENSE: -------------------------------------------------------------------------------- 1 | CC0 1.0 Universal 2 | 3 | Statement of Purpose 4 | 5 | The laws of most jurisdictions throughout the world automatically confer 6 | exclusive Copyright and Related Rights (defined below) upon the creator and 7 | subsequent owner(s) (each and all, an "owner") of an original work of 8 | authorship and/or a database (each, a "Work"). 9 | 10 | Certain owners wish to permanently relinquish those rights to a Work for the 11 | purpose of contributing to a commons of creative, cultural and scientific 12 | works ("Commons") that the public can reliably and without fear of later 13 | claims of infringement build upon, modify, incorporate in other works, reuse 14 | and redistribute as freely as possible in any form whatsoever and for any 15 | purposes, including without limitation commercial purposes. These owners may 16 | contribute to the Commons to promote the ideal of a free culture and the 17 | further production of creative, cultural and scientific works, or to gain 18 | reputation or greater distribution for their Work in part through the use and 19 | efforts of others. 20 | 21 | For these and/or other purposes and motivations, and without any expectation 22 | of additional consideration or compensation, the person associating CC0 with a 23 | Work (the "Affirmer"), to the extent that he or she is an owner of Copyright 24 | and Related Rights in the Work, voluntarily elects to apply CC0 to the Work 25 | and publicly distribute the Work under its terms, with knowledge of his or her 26 | Copyright and Related Rights in the Work and the meaning and intended legal 27 | effect of CC0 on those rights. 28 | 29 | 1. Copyright and Related Rights. A Work made available under CC0 may be 30 | protected by copyright and related or neighboring rights ("Copyright and 31 | Related Rights"). Copyright and Related Rights include, but are not limited 32 | to, the following: 33 | 34 | i. the right to reproduce, adapt, distribute, perform, display, communicate, 35 | and translate a Work; 36 | 37 | ii. moral rights retained by the original author(s) and/or performer(s); 38 | 39 | iii. publicity and privacy rights pertaining to a person's image or likeness 40 | depicted in a Work; 41 | 42 | iv. rights protecting against unfair competition in regards to a Work, 43 | subject to the limitations in paragraph 4(a), below; 44 | 45 | v. rights protecting the extraction, dissemination, use and reuse of data in 46 | a Work; 47 | 48 | vi. database rights (such as those arising under Directive 96/9/EC of the 49 | European Parliament and of the Council of 11 March 1996 on the legal 50 | protection of databases, and under any national implementation thereof, 51 | including any amended or successor version of such directive); and 52 | 53 | vii. other similar, equivalent or corresponding rights throughout the world 54 | based on applicable law or treaty, and any national implementations thereof. 55 | 56 | 2. Waiver. To the greatest extent permitted by, but not in contravention of, 57 | applicable law, Affirmer hereby overtly, fully, permanently, irrevocably and 58 | unconditionally waives, abandons, and surrenders all of Affirmer's Copyright 59 | and Related Rights and associated claims and causes of action, whether now 60 | known or unknown (including existing as well as future claims and causes of 61 | action), in the Work (i) in all territories worldwide, (ii) for the maximum 62 | duration provided by applicable law or treaty (including future time 63 | extensions), (iii) in any current or future medium and for any number of 64 | copies, and (iv) for any purpose whatsoever, including without limitation 65 | commercial, advertising or promotional purposes (the "Waiver"). Affirmer makes 66 | the Waiver for the benefit of each member of the public at large and to the 67 | detriment of Affirmer's heirs and successors, fully intending that such Waiver 68 | shall not be subject to revocation, rescission, cancellation, termination, or 69 | any other legal or equitable action to disrupt the quiet enjoyment of the Work 70 | by the public as contemplated by Affirmer's express Statement of Purpose. 71 | 72 | 3. Public License Fallback. Should any part of the Waiver for any reason be 73 | judged legally invalid or ineffective under applicable law, then the Waiver 74 | shall be preserved to the maximum extent permitted taking into account 75 | Affirmer's express Statement of Purpose. In addition, to the extent the Waiver 76 | is so judged Affirmer hereby grants to each affected person a royalty-free, 77 | non transferable, non sublicensable, non exclusive, irrevocable and 78 | unconditional license to exercise Affirmer's Copyright and Related Rights in 79 | the Work (i) in all territories worldwide, (ii) for the maximum duration 80 | provided by applicable law or treaty (including future time extensions), (iii) 81 | in any current or future medium and for any number of copies, and (iv) for any 82 | purpose whatsoever, including without limitation commercial, advertising or 83 | promotional purposes (the "License"). The License shall be deemed effective as 84 | of the date CC0 was applied by Affirmer to the Work. Should any part of the 85 | License for any reason be judged legally invalid or ineffective under 86 | applicable law, such partial invalidity or ineffectiveness shall not 87 | invalidate the remainder of the License, and in such case Affirmer hereby 88 | affirms that he or she will not (i) exercise any of his or her remaining 89 | Copyright and Related Rights in the Work or (ii) assert any associated claims 90 | and causes of action with respect to the Work, in either case contrary to 91 | Affirmer's express Statement of Purpose. 92 | 93 | 4. Limitations and Disclaimers. 94 | 95 | a. No trademark or patent rights held by Affirmer are waived, abandoned, 96 | surrendered, licensed or otherwise affected by this document. 97 | 98 | b. Affirmer offers the Work as-is and makes no representations or warranties 99 | of any kind concerning the Work, express, implied, statutory or otherwise, 100 | including without limitation warranties of title, merchantability, fitness 101 | for a particular purpose, non infringement, or the absence of latent or 102 | other defects, accuracy, or the present or absence of errors, whether or not 103 | discoverable, all to the greatest extent permissible under applicable law. 104 | 105 | c. Affirmer disclaims responsibility for clearing rights of other persons 106 | that may apply to the Work or any use thereof, including without limitation 107 | any person's Copyright and Related Rights in the Work. Further, Affirmer 108 | disclaims responsibility for obtaining any necessary consents, permissions 109 | or other rights required for any use of the Work. 110 | 111 | d. Affirmer understands and acknowledges that Creative Commons is not a 112 | party to this document and has no duty or obligation with respect to this 113 | CC0 or use of the Work. 114 | 115 | For more information, please see 116 | 117 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Machine Learning & Deep Learning Tutorials [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 3 | 4 | - 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://github.com/sindresorhus/awesome). 5 | 6 | - If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). 7 | 8 | - [Curated list of R tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataScienceR). 9 | 10 | - [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython). 11 | 12 | 13 | ## Contents 14 | - [Introduction](#general) 15 | - [Interview Resources](#interview) 16 | - [Artificial Intelligence](#ai) 17 | - [Genetic Algorithms](#ga) 18 | - [Statistics](#stat) 19 | - [Useful Blogs](#blogs) 20 | - [Resources on Quora](#quora) 21 | - [Resources on Kaggle](#kaggle) 22 | - [Cheat Sheets](#cs) 23 | - [Classification](#classification) 24 | - [Linear Regression](#linear) 25 | - [Logistic Regression](#logistic) 26 | - [Model Validation using Resampling](#validation) 27 | - [Cross Validation](#cross) 28 | - [Bootstraping](#boot) 29 | - [Deep Learning](#deep) 30 | - [Frameworks](#frame) 31 | - [Feed Forward Networks](#feed) 32 | - [Recurrent Neural Nets, LSTM, GRU](#rnn) 33 | - [Restricted Boltzmann Machine, DBNs](#rbm) 34 | - [Autoencoders](#auto) 35 | - [Convolutional Neural Nets](#cnn) 36 | - [Graph Representation Learning](#nrl) 37 | - [Natural Language Processing](#nlp) 38 | - [Topic Modeling, LDA](#topic) 39 | - [Word2Vec](#word2vec) 40 | - [Computer Vision](#vision) 41 | - [Support Vector Machine](#svm) 42 | - [Reinforcement Learning](#rl) 43 | - [Decision Trees](#dt) 44 | - [Random Forest / Bagging](#rf) 45 | - [Boosting](#gbm) 46 | - [Ensembles](#ensem) 47 | - [Stacking Models](#stack) 48 | - [VC Dimension](#vc) 49 | - [Bayesian Machine Learning](#bayes) 50 | - [Semi Supervised Learning](#semi) 51 | - [Optimizations](#opt) 52 | - [Other Useful Tutorials](#other) 53 | 54 | 55 | 56 | ## Introduction 57 | 58 | - [Machine Learning Course by Andrew Ng (Stanford University)](https://www.coursera.org/learn/machine-learning) 59 | 60 | - [AI/ML YouTube Courses](https://github.com/dair-ai/ML-YouTube-Courses) 61 | 62 | - [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml) 63 | 64 | - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) 65 | 66 | - [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) 67 | 68 | - [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) 69 | 70 | - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) 71 | 72 | - [Dive into Machine Learning](https://github.com/hangtwenty/dive-into-machine-learning) 73 | 74 | - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) 75 | 76 | - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) 77 | 78 | - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) 79 | 80 | - [The Open Source Data Science Masters](http://datasciencemasters.org/) 81 | 82 | - [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) 83 | 84 | - [Machine Learning algorithms that you should always have a strong understanding of](https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why) 85 | 86 | - [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf) 87 | 88 | - [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) 89 | 90 | - [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) 91 | 92 | - [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) 93 | 94 | - [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) 95 | 96 | - [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) 97 | 98 | - [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) 99 | 100 | - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) 101 | 102 | - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) 103 | 104 | - [Have Fun With Machine Learning](https://github.com/humphd/have-fun-with-machine-learning) 105 | 106 | - [Twitter's Most Shared #machineLearning Content From The Past 7 Days](http://theherdlocker.com/tweet/popularity/machinelearning) 107 | 108 | - [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning) 109 | 110 | 111 | 112 | ## Interview Resources 113 | 114 | - [41 Essential Machine Learning Interview Questions (with answers)](https://www.springboard.com/blog/machine-learning-interview-questions/) 115 | 116 | - [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-intern-interviews) 117 | 118 | - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) 119 | 120 | - [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) 121 | 122 | - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) 123 | 124 | - [The Big List of DS/ML Interview Resources](https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63) 125 | 126 | 127 | 128 | ## Artificial Intelligence 129 | 130 | - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) 131 | 132 | - [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) 133 | 134 | - [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai) 135 | 136 | - [MIT 6.034 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/) 137 | 138 | - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) 139 | 140 | - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) 141 | 142 | - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) 143 | 144 | 145 | 146 | ## Genetic Algorithms 147 | 148 | - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) 149 | 150 | - [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/miniga.html), [Part 2](http://outlace.com/miniga_addendum.html) 151 | 152 | - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) 153 | 154 | - [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) 155 | 156 | - [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) 157 | 158 | - [Genetic Programming in Python (GitHub)](https://github.com/trevorstephens/gplearn) 159 | 160 | - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](http://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming) 161 | 162 | 163 | 164 | ## Statistics 165 | 166 | - [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics 167 | 168 | - [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach 169 | 170 | - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas 171 | 172 | - [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics 173 | 174 | - [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) 175 | 176 | - Tutorials 177 | 178 | - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) 179 | 180 | - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) 181 | 182 | - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) 183 | 184 | - [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) 185 | 186 | - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) 187 | 188 | - [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) 189 | 190 | - [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook 191 | 192 | 193 | 194 | ## Useful Blogs 195 | 196 | - [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science 197 | 198 | - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! 199 | 200 | - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning 201 | 202 | - [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general 203 | 204 | - [Colah's Blog](http://colah.github.io/) - Awesome Neural Networks Blog 205 | 206 | - [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering 207 | 208 | - [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog 209 | 210 | - [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors 211 | 212 | - [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond 213 | 214 | - [fastML](http://fastml.com/) - Machine learning made easy 215 | 216 | - [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page 217 | 218 | - [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science 219 | 220 | - [A Quantitative Journey | outlace](http://outlace.com/) - learning quantitative applications 221 | 222 | - [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R 223 | 224 | - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog 225 | 226 | - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence 227 | 228 | - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) - Making deep learning accessible 229 | 230 | - [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets 231 | 232 | - [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning 233 | 234 | - [Ethen's Notebook Collection](https://github.com/ethen8181/machine-learning) - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage 235 | 236 | 237 | 238 | ## Resources on Quora 239 | 240 | - [Most Viewed Machine Learning writers](https://www.quora.com/topic/Machine-Learning/writers) 241 | 242 | - [Data Science Topic on Quora](https://www.quora.com/Data-Science) 243 | 244 | - [William Chen's Answers](https://www.quora.com/William-Chen-6/answers) 245 | 246 | - [Michael Hochster's Answers](https://www.quora.com/Michael-Hochster/answers) 247 | 248 | - [Ricardo Vladimiro's Answers](https://www.quora.com/Ricardo-Vladimiro-1/answers) 249 | 250 | - [Storytelling with Statistics](https://datastories.quora.com/) 251 | 252 | - [Data Science FAQs on Quora](https://www.quora.com/topic/Data-Science/faq) 253 | 254 | - [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq) 255 | 256 | 257 | 258 | ## Kaggle Competitions WriteUp 259 | 260 | - [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) 261 | 262 | - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) 263 | 264 | - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) 265 | 266 | - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) 267 | 268 | - [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/) 269 | 270 | 271 | 272 | ## Cheat Sheets 273 | 274 | - [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), 275 | [Source](http://www.wzchen.com/probability-cheatsheet/) 276 | 277 | - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) 278 | 279 | - [ML Compiled](https://ml-compiled.readthedocs.io/en/latest/) 280 | 281 | 282 | 283 | ## Classification 284 | 285 | - [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/) 286 | 287 | - [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) 288 | 289 | - [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) 290 | 291 | - [What are the advantages of different classification algorithms?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms) 292 | 293 | - [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) ([related video](https://youtu.be/OAl6eAyP-yo)) 294 | 295 | - [An introduction to ROC analysis](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf) 296 | 297 | - [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) 298 | 299 | 300 | 301 | 302 | ## Linear Regression 303 | 304 | - [General](#general-) 305 | 306 | - [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) 307 | 308 | - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm) 309 | 310 | - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) 311 | 312 | - [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) 313 | 314 | - [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) 315 | 316 | - [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) 317 | - Multicollinearity and VIF 318 | 319 | - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) 320 | 321 | - [Dealing with multicollinearity using VIFs](https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/) 322 | 323 | - [Residual Analysis](#residuals-) 324 | 325 | - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm) 326 | 327 | - [How to interpret a QQ plot?](http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1) 328 | 329 | - [Interpreting Residuals vs Fitted Plot](http://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions) 330 | 331 | - [Outliers](#outliers-) 332 | 333 | - [How should outliers be dealt with?](http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis) 334 | 335 | - [Elastic Net](https://en.wikipedia.org/wiki/Elastic_net_regularization) 336 | - [Regularization and Variable Selection via the 337 | Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) 338 | 339 | 340 | 341 | ## Logistic Regression 342 | 343 | - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) 344 | 345 | - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) 346 | 347 | - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) 348 | 349 | - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) 350 | 351 | - [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) 352 | 353 | - [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) 354 | 355 | - [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) 356 | 357 | 358 | 359 | ## Model Validation using Resampling 360 | 361 | - [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics)) 362 | 363 | - [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations) 364 | 365 | - [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-and.html) 366 | 367 | 368 | 369 | - [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) 370 | - [How to use cross-validation in predictive modeling](http://stuartlacy.co.uk/2016/02/04/how-to-correctly-use-cross-validation-in-predictive-modelling/) 371 | - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) 372 | 373 | - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) 374 | 375 | - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation) 376 | 377 | - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set) 378 | 379 | - [Choice of k in k-fold CV](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation) 380 | 381 | - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning) 382 | 383 | - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret) 384 | 385 | - [Good Resources](http://www.chioka.in/tag/cross-validation/) 386 | 387 | - Overfitting and Cross Validation 388 | 389 | - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf) 390 | 391 | - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf) 392 | 393 | - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf) 394 | 395 | - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem) 396 | 397 | 398 | 399 | 400 | - [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) 401 | 402 | - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works) 403 | 404 | - [Good Animation](https://www.stat.auckland.ac.nz/~wild/BootAnim/) 405 | 406 | - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm) 407 | 408 | - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1) 409 | 410 | - [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) 411 | 412 | 413 | 414 | 415 | ## Deep Learning 416 | 417 | - [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/) 418 | 419 | - [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html) 420 | 421 | - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) 422 | 423 | - **[Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)** 424 | 425 | - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) 426 | 427 | - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) 428 | 429 | - [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) 430 | 431 | - [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 432 | 433 | - [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) 434 | 435 | - [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) 436 | 437 | - [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) 438 | 439 | - [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/) 440 | 441 | - [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) 442 | 443 | - [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) 444 | 445 | - [Introduction to Deep Learning Using Python (GitHub)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) 446 | 447 | - [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) 448 | 449 | - [Deep Learning Software List](http://deeplearning.net/software_links/) 450 | 451 | - [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) 452 | 453 | - [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) 454 | 455 | - [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) 456 | 457 | - [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) 458 | 459 | - [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) 460 | 461 | - [deeplearning Tutorials](http://deeplearning4j.org/) 462 | 463 | - [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) 464 | 465 | - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) 466 | 467 | - [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c) 468 | 469 | - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) 470 | 471 | - [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) 472 | 473 | - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) 474 | 475 | - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) 476 | 477 | - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) 478 | 479 | - [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) 480 | 481 | - Neural Machine Translation 482 | 483 | - **[Machine Translation Reading List](https://github.com/THUNLP-MT/MT-Reading-List#machine-translation-reading-list)** 484 | 485 | - [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/) 486 | 487 | - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) 488 | 489 | 490 | 491 | - Deep Learning Frameworks 492 | 493 | - [Torch vs. Theano](http://fastml.com/torch-vs-theano/) 494 | 495 | - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) 496 | 497 | - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) 498 | 499 | 500 | - [Theano](https://en.wikipedia.org/wiki/Theano_(software)) 501 | 502 | - [Website](http://deeplearning.net/software/theano/) 503 | 504 | - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) 505 | 506 | - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/) 507 | 508 | - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/) 509 | 510 | - [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg) 511 | 512 | - [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp) 513 | 514 | - [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet) 515 | 516 | - [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu) 517 | 518 | - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) 519 | 520 | - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) 521 | 522 | - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) 523 | 524 | - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) 525 | 526 | - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) 527 | 528 | - [Torch](http://torch.ch/) 529 | 530 | - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) 531 | 532 | - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) 533 | 534 | - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) 535 | 536 | - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch) 537 | 538 | - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) 539 | 540 | - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) 541 | 542 | - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet) 543 | 544 | - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 545 | 546 | - Caffe 547 | - [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) 548 | 549 | - TensorFlow 550 | - [Website](http://tensorflow.org/) 551 | 552 | - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) 553 | 554 | - [Stanford Tensorflow for Deep Learning Research Course](https://web.stanford.edu/class/cs20si/syllabus.html) 555 | 556 | - [GitHub Repo](https://github.com/chiphuyen/tf-stanford-tutorials) 557 | 558 | - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) 559 | 560 | - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) 561 | 562 | - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) 563 | 564 | - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) 565 | 566 | - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book) 567 | 568 | - [Android TensorFlow Machine Learning Example](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) 569 | 570 | - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample) 571 | - [Creating Custom Model For Android Using TensorFlow](https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb) 572 | - [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample) 573 | 574 | 575 | 576 | - Feed Forward Networks 577 | 578 | - [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) 579 | 580 | - [Implementing a Neural Network from scratch](http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/), [Code](https://github.com/dennybritz/nn-from-scratch) 581 | 582 | - [Speeding up your Neural Network with Theano and the gpu](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/), [Code](https://github.com/dennybritz/nn-theano) 583 | 584 | - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) 585 | 586 | - [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) 587 | 588 | - [Choosing number of hidden layers and nodes](http://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](http://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#) 589 | 590 | - [Backpropagation in Matrix Form](http://sudeepraja.github.io/Neural/) 591 | 592 | - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) 593 | 594 | - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) 595 | 596 | - [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) 597 | 598 | - [Regression and Classification with NNs (Slides)](http://www.autonlab.org/tutorials/neural13.pdf) 599 | 600 | - [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) 601 | 602 | 603 | 604 | - Recurrent and LSTM Networks 605 | - [awesome-rnn: list of resources (GitHub Repo)](https://github.com/kjw0612/awesome-rnn) 606 | 607 | - [Recurrent Neural Net Tutorial Part 1](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/) 608 | 609 | - [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) 610 | 611 | - [The Unreasonable effectiveness of RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), [Torch Code](https://github.com/karpathy/char-rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086) 612 | 613 | - [Intro to RNN](http://deeplearning4j.org/recurrentnetwork.html), [LSTM](http://deeplearning4j.org/lstm.html) 614 | 615 | - [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) 616 | 617 | - [Optimizing RNN Performance](http://svail.github.io/) 618 | 619 | - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) 620 | 621 | - [Auto-Generating Clickbait with RNN](https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) 622 | 623 | - [Sequence Learning using RNN (Slides)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) 624 | 625 | - [Machine Translation using RNN (Paper)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) 626 | 627 | - [Music generation using RNNs (Keras)](https://github.com/MattVitelli/GRUV) 628 | 629 | - [Using RNN to create on-the-fly dialogue (Keras)](http://neuralniche.com/post/tutorial/) 630 | 631 | - Long Short Term Memory (LSTM) 632 | 633 | - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) 634 | 635 | - [LSTM explained](https://apaszke.github.io/lstm-explained.html) 636 | 637 | - [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html) 638 | 639 | - [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/), [Python/Theano code](https://github.com/dennybritz/rnn-tutorial-gru-lstm) 640 | 641 | - [Torch Code for character-level language models using LSTM](https://github.com/karpathy/char-rnn) 642 | 643 | - [LSTM for Kaggle EEG Detection competition (Torch Code)](https://github.com/apaszke/kaggle-grasp-and-lift) 644 | 645 | - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) 646 | 647 | - [Deep Learning for Visual Q&A | LSTM | CNN](http://avisingh599.github.io/deeplearning/visual-qa/), [Code](https://github.com/avisingh599/visual-qa) 648 | 649 | - [Computer Responds to email using LSTM | Google](http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html) 650 | 651 | - [LSTM dramatically improves Google Voice Search](http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html), [Another Article](http://deeplearning.net/2015/09/30/long-short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/) 652 | 653 | - [Understanding Natural Language with LSTM Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 654 | 655 | - [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) 656 | 657 | - [LSTM for Human Activity Recognition](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/) 658 | 659 | - Gated Recurrent Units (GRU) 660 | 661 | - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) 662 | 663 | - [Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) 664 | 665 | 666 | 667 | 668 | - [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network) 669 | 670 | - [Recursive Neural Tensor Network (RNTN)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) 671 | 672 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 673 | 674 | 675 | 676 | - Restricted Boltzmann Machine 677 | 678 | - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) 679 | 680 | - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) 681 | 682 | - [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) 683 | 684 | - [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) 685 | 686 | - [RBMs in R](https://github.com/zachmayer/rbm) 687 | 688 | - [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) 689 | 690 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 691 | 692 | 693 | 694 | - Autoencoders: Unsupervised (applies BackProp after setting target = input) 695 | 696 | - [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) 697 | 698 | - [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) 699 | 700 | - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) 701 | 702 | - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) 703 | 704 | 705 | 706 | 707 | - Convolutional Neural Networks 708 | 709 | - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) 710 | 711 | - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) 712 | 713 | - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) 714 | 715 | - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) 716 | 717 | - [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) 718 | 719 | - [JavaScript Library (Browser Based) for CNNs](http://cs.stanford.edu/people/karpathy/convnetjs/) 720 | 721 | - [Using CNNs to detect facial keypoints](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) 722 | 723 | - [Deep learning to classify business photos at Yelp](http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html) 724 | 725 | - [Interview with Yann LeCun | Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) 726 | 727 | - [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) 728 | 729 | 730 | 731 | - Network Representation Learning 732 | 733 | - [Awesome Graph Embedding](https://github.com/benedekrozemberczki/awesome-graph-embedding) 734 | 735 | - [Awesome Network Embedding](https://github.com/chihming/awesome-network-embedding) 736 | 737 | - [Network Representation Learning Papers](https://github.com/thunlp) 738 | 739 | - [Knowledge Representation Learning Papers](https://github.com/thunlp/KRLPapers) 740 | 741 | - [Graph Based Deep Learning Literature](https://github.com/naganandy/graph-based-deep-learning-literature) 742 | 743 | 744 | 745 | ## Natural Language Processing 746 | 747 | - [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) 748 | 749 | - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 750 | 751 | - [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/) 752 | 753 | - [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) 754 | 755 | - [The Stanford NLP Group](https://nlp.stanford.edu/) 756 | 757 | - [NLP from Scratch | Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) 758 | 759 | - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) 760 | 761 | - [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) 762 | 763 | - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) 764 | 765 | 766 | 767 | - Topic Modeling 768 | - [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model) 769 | - [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf) 770 | 771 | - [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) 772 | 773 | - [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation) 774 | 775 | - [**Introduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html) 776 | 777 | - [The LDA Buffet - Intuitive Explanation](http://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/) 778 | 779 | - [Your Guide to Latent Dirichlet Allocation (LDA)](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d) 780 | 781 | - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) 782 | 783 | - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) 784 | 785 | - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) 786 | 787 | - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) 788 | - [topicmodels: An R Package for Fitting Topic Models](https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf) 789 | 790 | - [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) 791 | 792 | - [Online LDA](http://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](http://alexminnaar.com/distributed-online-latent-dirichlet-allocation-with-apache-spark.html) 793 | 794 | - [LDA in Scala](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](http://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html) 795 | 796 | - [Segmentation of Twitter Timelines via Topic Modeling](https://alexisperrier.com/nlp/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) 797 | 798 | - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) 799 | 800 | - [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb)) 801 | 802 | - [Deep Belief Nets for Topic Modeling](https://github.com/larsmaaloee/deep-belief-nets-for-topic-modeling) 803 | - [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf) 804 | - Python 805 | - [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note) 806 | - [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model) 807 | 808 | 809 | 810 | - word2vec 811 | 812 | - [Google word2vec](https://code.google.com/archive/p/word2vec) 813 | 814 | - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) 815 | 816 | - [word2vec Tutorial](https://rare-technologies.com/word2vec-tutorial/) 817 | 818 | - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) 819 | 820 | - [Skip Gram Model Tutorial](http://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](http://alexminnaar.com/word2vec-tutorial-part-ii-the-continuous-bag-of-words-model.html) 821 | 822 | - [Word Vectors Kaggle Tutorial Python](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) 823 | 824 | - [Making sense of word2vec](http://rare-technologies.com/making-sense-of-word2vec/) 825 | 826 | - [word2vec explained on deeplearning4j](http://deeplearning4j.org/word2vec.html) 827 | 828 | - [Quora word2vec](https://www.quora.com/How-does-word2vec-work) 829 | 830 | - [Other Quora Resources](https://www.quora.com/What-are-the-continuous-bag-of-words-and-skip-gram-architectures-in-laymans-terms), [2](https://www.quora.com/What-is-the-difference-between-the-Bag-of-Words-model-and-the-Continuous-Bag-of-Words-model), [3](https://www.quora.com/Is-skip-gram-negative-sampling-better-than-CBOW-NS-for-word2vec-If-so-why) 831 | 832 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 833 | 834 | - Text Clustering 835 | 836 | - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) 837 | 838 | - [Levenshtein distance for measuring the difference between two sequences](https://en.wikipedia.org/wiki/Levenshtein_distance) 839 | 840 | - [Text clustering with Levenshtein distances](http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances) 841 | 842 | - Text Classification 843 | 844 | - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) 845 | 846 | - Named Entity Recognitation 847 | 848 | - [Stanford Named Entity Recognizer (NER)](https://nlp.stanford.edu/software/CRF-NER.shtml) 849 | 850 | - [Named Entity Recognition: Applications and Use Cases- Towards Data Science](https://towardsdatascience.com/named-entity-recognition-applications-and-use-cases-acdbf57d595e) 851 | 852 | - [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) 853 | 854 | - [Kaggle Tutorial Bag of Words and Word vectors](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words), [Part 2](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors), [Part 3](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-3-more-fun-with-word-vectors) 855 | 856 | - [What would Shakespeare say (NLP Tutorial)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) 857 | 858 | - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) 859 | 860 | 861 | 862 | ## Computer Vision 863 | - [Awesome computer vision (github)](https://github.com/jbhuang0604/awesome-computer-vision) 864 | 865 | - [Awesome deep vision (github)](https://github.com/kjw0612/awesome-deep-vision) 866 | 867 | 868 | 869 | 870 | ## Support Vector Machine 871 | 872 | - [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm) 873 | 874 | - [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) 875 | 876 | - [SVM in Layman's terms](https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms) 877 | 878 | - [How does SVM Work | Comparisons](http://stats.stackexchange.com/questions/23391/how-does-a-support-vector-machine-svm-work) 879 | 880 | - [A tutorial on SVMs](http://alex.smola.org/papers/2003/SmoSch03b.pdf) 881 | 882 | - [Practical Guide to SVC](http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf), [Slides](http://www.csie.ntu.edu.tw/~cjlin/talks/freiburg.pdf) 883 | 884 | - [Introductory Overview of SVMs](http://www.statsoft.com/Textbook/Support-Vector-Machines) 885 | 886 | - Comparisons 887 | 888 | - [SVMs > ANNs](http://stackoverflow.com/questions/6699222/support-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](http://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](http://www.svms.org/anns.html) 889 | 890 | - [Trees > SVMs](http://stats.stackexchange.com/questions/57438/why-is-svm-not-so-good-as-decision-tree-on-the-same-data) 891 | 892 | - [Kernel Logistic Regression vs SVM](http://stats.stackexchange.com/questions/43996/kernel-logistic-regression-vs-svm) 893 | 894 | - [Logistic Regression vs SVM](http://stats.stackexchange.com/questions/58684/regularized-logistic-regression-and-support-vector-machine), [2](http://stats.stackexchange.com/questions/95340/svm-v-s-logistic-regression), [3](https://www.quora.com/Support-Vector-Machines/What-is-the-difference-between-Linear-SVMs-and-Logistic-Regression) 895 | 896 | - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) 897 | 898 | - [Variable Importance from SVM](http://stats.stackexchange.com/questions/2179/variable-importance-from-svm) 899 | 900 | - Software 901 | 902 | - [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) 903 | 904 | - [Intro to SVM in R](http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf) 905 | 906 | - Kernels 907 | - [What are Kernels in ML and SVM?](https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM) 908 | 909 | - [Intuition Behind Gaussian Kernel in SVMs?](https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM) 910 | 911 | - Probabilities post SVM 912 | 913 | - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf) 914 | 915 | - [Platt Calibration Wiki](https://en.wikipedia.org/wiki/Platt_scaling) 916 | 917 | - [Why use Platts Scaling](http://stats.stackexchange.com/questions/5196/why-use-platts-scaling) 918 | 919 | - [Classifier Classification with Platt's Scaling](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) 920 | 921 | 922 | 923 | 924 | ## Reinforcement Learning 925 | 926 | - [Awesome Reinforcement Learning (GitHub)](https://github.com/aikorea/awesome-rl) 927 | 928 | - [RL Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) 929 | 930 | 931 | 932 | ## Decision Trees 933 | 934 | - [Wikipedia Page - Lots of Good Info](https://en.wikipedia.org/wiki/Decision_tree_learning) 935 | 936 | - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) 937 | 938 | - [Brief Tour of Trees and Forests](https://statistical-research.com/index.php/2013/04/29/a-brief-tour-of-the-trees-and-forests/) 939 | 940 | - [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html) 941 | 942 | - [How Decision Trees work?](http://www.aihorizon.com/essays/generalai/decision_trees.htm) 943 | 944 | - [Weak side of Decision Trees](http://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees) 945 | 946 | - [Thorough Explanation and different algorithms](http://www.ise.bgu.ac.il/faculty/liorr/hbchap9.pdf) 947 | 948 | - [What is entropy and information gain in the context of building decision trees?](http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain) 949 | 950 | - [Slides Related to Decision Trees](http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees) 951 | 952 | - [How do decision tree learning algorithms deal with missing values?](http://stats.stackexchange.com/questions/96025/how-do-decision-tree-learning-algorithms-deal-with-missing-values-under-the-hoo) 953 | 954 | - [Using Surrogates to Improve Datasets with Missing Values](https://www.salford-systems.com/videos/tutorials/tips-and-tricks/using-surrogates-to-improve-datasets-with-missing-values) 955 | 956 | - [Good Article](https://www.mindtools.com/dectree.html) 957 | 958 | - [Are decision trees almost always binary trees?](http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees) 959 | 960 | - [Pruning Decision Trees](https://en.wikipedia.org/wiki/Pruning_(decision_trees)), [Grafting of Decision Trees](https://en.wikipedia.org/wiki/Grafting_(decision_trees)) 961 | 962 | - [What is Deviance in context of Decision Trees?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) 963 | 964 | - [Discover structure behind data with decision trees](http://vooban.com/en/tips-articles-geek-stuff/discover-structure-behind-data-with-decision-trees/) - Grow and plot a decision tree to automatically figure out hidden rules in your data 965 | 966 | - Comparison of Different Algorithms 967 | 968 | - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees) 969 | 970 | - [Comparison of complexity or performance](https://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance) 971 | 972 | - [CHAID vs CART](http://stats.stackexchange.com/questions/61230/chaid-vs-crt-or-cart) , [CART vs CHAID](http://www.bzst.com/2006/10/classification-trees-cart-vs-chaid.html) 973 | 974 | - [Good Article on comparison](http://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11) 975 | 976 | - CART 977 | 978 | - [Recursive Partitioning Wikipedia](https://en.wikipedia.org/wiki/Recursive_partitioning) 979 | 980 | - [CART Explained](http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees) 981 | 982 | - [How to measure/rank “variable importance” when using CART?](http://stats.stackexchange.com/questions/6478/how-to-measure-rank-variable-importance-when-using-cart-specifically-using) 983 | 984 | - [Pruning a Tree in R](http://stackoverflow.com/questions/15318409/how-to-prune-a-tree-in-r) 985 | 986 | - [Does rpart use multivariate splits by default?](http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default) 987 | 988 | - [FAQs about Recursive Partitioning](http://stats.stackexchange.com/questions/tagged/rpart) 989 | 990 | - CTREE 991 | 992 | - [party package in R](https://cran.r-project.org/web/packages/party/party.pdf) 993 | 994 | - [Show volumne in each node using ctree in R](http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r) 995 | 996 | - [How to extract tree structure from ctree function?](http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function) 997 | 998 | - CHAID 999 | 1000 | - [Wikipedia Artice on CHAID](https://en.wikipedia.org/wiki/CHAID) 1001 | 1002 | - [Basic Introduction to CHAID](https://smartdrill.com/Introduction-to-CHAID.html) 1003 | 1004 | - [Good Tutorial on CHAID](http://www.statsoft.com/Textbook/CHAID-Analysis) 1005 | 1006 | - MARS 1007 | 1008 | - [Wikipedia Article on MARS](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) 1009 | 1010 | - Probabilistic Decision Trees 1011 | 1012 | - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) 1013 | 1014 | - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) 1015 | 1016 | 1017 | 1018 | ## Random Forest / Bagging 1019 | 1020 | - [Awesome Random Forest (GitHub)**](https://github.com/kjw0612/awesome-random-forest) 1021 | 1022 | - [How to tune RF parameters in practice?](https://www.kaggle.com/forums/f/15/kaggle-forum/t/4092/how-to-tune-rf-parameters-in-practice) 1023 | 1024 | - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) 1025 | 1026 | - [Compare R-squared from two different Random Forest models](http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models) 1027 | 1028 | - [OOB Estimate Explained | RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) 1029 | 1030 | - [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](https://www.jstatsoft.org/index.php/jss/article/view/v050i11) 1031 | 1032 | - [Why doesn't Random Forest handle missing values in predictors?](http://stats.stackexchange.com/questions/98953/why-doesnt-random-forest-handle-missing-values-in-predictors) 1033 | 1034 | - [How to build random forests in R with missing (NA) values?](http://stackoverflow.com/questions/8370455/how-to-build-random-forests-in-r-with-missing-na-values) 1035 | 1036 | - [FAQs about Random Forest](http://stats.stackexchange.com/questions/tagged/random-forest), [More FAQs](http://stackoverflow.com/questions/tagged/random-forest) 1037 | 1038 | - [Obtaining knowledge from a random forest](http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest) 1039 | 1040 | - [Some Questions for R implementation](http://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](http://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](http://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact) 1041 | 1042 | 1043 | 1044 | ## Boosting 1045 | 1046 | - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) 1047 | 1048 | - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) 1049 | 1050 | - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) 1051 | 1052 | - Gradient Boosting Machine 1053 | 1054 | - [Gradiet Boosting Wiki](https://en.wikipedia.org/wiki/Gradient_boosting) 1055 | 1056 | - [Guidelines for GBM parameters in R](http://stats.stackexchange.com/questions/25748/what-are-some-useful-guidelines-for-gbm-parameters), [Strategy to set parameters](http://stats.stackexchange.com/questions/35984/strategy-to-set-the-gbm-parameters) 1057 | 1058 | - [Meaning of Interaction Depth](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm), [2](http://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm) 1059 | 1060 | - [Role of n.minobsinnode parameter of GBM in R](http://stats.stackexchange.com/questions/30645/role-of-n-minobsinnode-parameter-of-gbm-in-r) 1061 | 1062 | - [GBM in R](http://www.slideshare.net/mark_landry/gbm-package-in-r) 1063 | 1064 | - [FAQs about GBM](http://stats.stackexchange.com/tags/gbm/hot) 1065 | 1066 | - [GBM vs xgboost](https://www.kaggle.com/c/higgs-boson/forums/t/9497/r-s-gbm-vs-python-s-xgboost) 1067 | 1068 | - xgboost 1069 | 1070 | - [xgboost tuning kaggle](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log) 1071 | 1072 | - [xgboost vs gbm](https://www.kaggle.com/c/otto-group-product-classification-challenge/forums/t/13012/question-to-experienced-kagglers-and-anyone-who-wants-to-take-a-shot/68296#post68296) 1073 | 1074 | - [xgboost survey](https://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey) 1075 | 1076 | - [Practical XGBoost in Python online course (free)](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) 1077 | 1078 | - AdaBoost 1079 | 1080 | - [AdaBoost Wiki](https://en.wikipedia.org/wiki/AdaBoost), [Python Code](https://gist.github.com/tristanwietsma/5486024) 1081 | 1082 | - [AdaBoost Sparse Input Support](http://hamzehal.blogspot.com/2014/06/adaboost-sparse-input-support.html) 1083 | 1084 | - [adaBag R package](https://cran.r-project.org/web/packages/adabag/adabag.pdf) 1085 | 1086 | - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) 1087 | 1088 | - CatBoost 1089 | 1090 | - [CatBoost Documentation](https://catboost.ai/docs/) 1091 | 1092 | - [Benchmarks](https://catboost.ai/#benchmark) 1093 | 1094 | - [Tutorial](https://github.com/catboost/tutorials) 1095 | 1096 | - [GitHub Project](https://github.com/catboost) 1097 | 1098 | - [CatBoost vs. Light GBM vs. XGBoost](https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db) 1099 | 1100 | 1101 | 1102 | ## Ensembles 1103 | 1104 | - [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) 1105 | 1106 | - [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) 1107 | 1108 | - [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) 1109 | 1110 | - [Ensemble Learning Intro](http://machine-learning.martinsewell.com/ensembles/) 1111 | 1112 | - [Ensemble Learning Paper](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf) 1113 | 1114 | - [Ensembling models with R](http://amunategui.github.io/blending-models/), [Ensembling Regression Models in R](http://stats.stackexchange.com/questions/26790/ensembling-regression-models), [Intro to Ensembles in R](http://www.vikparuchuri.com/blog/intro-to-ensemble-learning-in-r/) 1115 | 1116 | - [Ensembling Models with caret](http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret) 1117 | 1118 | - [Bagging vs Boosting vs Stacking](http://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning) 1119 | 1120 | - [Good Resources | Kaggle Africa Soil Property Prediction](https://www.kaggle.com/c/afsis-soil-properties/forums/t/10391/best-ensemble-references) 1121 | 1122 | - [Boosting vs Bagging](http://www.chioka.in/which-is-better-boosting-or-bagging/) 1123 | 1124 | - [Resources for learning how to implement ensemble methods](http://stats.stackexchange.com/questions/32703/resources-for-learning-how-to-implement-ensemble-methods) 1125 | 1126 | - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) 1127 | 1128 | 1129 | 1130 | ## Stacking Models 1131 | 1132 | - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) 1133 | 1134 | - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) 1135 | 1136 | - [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) 1137 | 1138 | - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) 1139 | 1140 | 1141 | 1142 | ## Vapnik–Chervonenkis Dimension 1143 | 1144 | - [Wikipedia article on VC Dimension](https://en.wikipedia.org/wiki/VC_dimension) 1145 | 1146 | - [Intuitive Explanantion of VC Dimension](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) 1147 | 1148 | - [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c) 1149 | 1150 | - [Introduction to VC Dimension](http://www.svms.org/vc-dimension/) 1151 | 1152 | - [FAQs about VC Dimension](http://stats.stackexchange.com/questions/tagged/vc-dimension) 1153 | 1154 | - [Do ensemble techniques increase VC-dimension?](http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension) 1155 | 1156 | 1157 | 1158 | 1159 | ## Bayesian Machine Learning 1160 | 1161 | - [Bayesian Methods for Hackers (using pyMC)](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) 1162 | 1163 | - [Should all Machine Learning be Bayesian?](http://videolectures.net/bark08_ghahramani_samlbb/) 1164 | 1165 | - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) 1166 | 1167 | - [Bayesian Reasoning and Deep Learning](http://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/), [Slides](http://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf) 1168 | 1169 | - [Bayesian Statistics Made Simple](http://greenteapress.com/wp/think-bayes/) 1170 | 1171 | - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) 1172 | 1173 | - [Markov Chain Wikipedia Page](https://en.wikipedia.org/wiki/Markov_chain) 1174 | 1175 | 1176 | 1177 | 1178 | ## Semi Supervised Learning 1179 | 1180 | - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) 1181 | 1182 | - [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) 1183 | 1184 | - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) 1185 | 1186 | - [Taxonomy](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].pdf) 1187 | 1188 | - [Video Tutorial Weka](https://www.youtube.com/watch?v=sWxcIjZFGNM) 1189 | 1190 | - [Unsupervised, Supervised and Semi Supervised learning](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) 1191 | 1192 | - [Research Papers 1](http://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](http://icml.cc/2012/papers/616.pdf) 1193 | 1194 | 1195 | 1196 | 1197 | ## Optimization 1198 | 1199 | - [Mean Variance Portfolio Optimization with R and Quadratic Programming](http://www.wdiam.com/2012/06/10/mean-variance-portfolio-optimization-with-r-and-quadratic-programming/?utm_content=buffer04c12&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer) 1200 | 1201 | - [Algorithms for Sparse Optimization and Machine Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) 1202 | 1203 | - [Optimization Algorithms in Machine Learning](http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf), [Video Lecture](http://videolectures.net/nips2010_wright_oaml/) 1204 | 1205 | - [Optimization Algorithms for Data Analysis](http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf) 1206 | 1207 | - [Video Lectures on Optimization](http://videolectures.net/stephen_j_wright/) 1208 | 1209 | - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) 1210 | 1211 | - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) 1212 | 1213 | - [Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters](http://vooban.com/en/tips-articles-geek-stuff/hyperopt-tutorial-for-optimizing-neural-networks-hyperparameters/) 1214 | 1215 | 1216 | 1217 | 1218 | ## Other Tutorials 1219 | 1220 | - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR). 1221 | 1222 | - For a collection of Data Science Tutorials using Python, please refer to [this list](https://github.com/ujjwalkarn/DataSciencePython). 1223 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | If you want to contribute to this list (please do), send me a pull request. Since we want this list to be useful in the long run, **please submit high quality links only**. 2 | 3 | ## Adding to this list 4 | 5 | Please ensure your pull request adheres to the following guidelines: 6 | 7 | - **Please make an individual pull request for each suggestion.** 8 | - Please only submit resources that are completly free to access. 9 | - The pull request and commit should have a useful title. 10 | - Please search previous suggestions before making a new one, as yours may be a duplicate. 11 | - Make sure your link has a useful and relevant title. 12 | - Please use [title-casing](http://titlecapitalization.com) (AP style). 13 | - Please use the following format: `[Useful Title](link)` 14 | - Link additions should be added to the bottom of the relevant category. 15 | - New categories or improvements to the existing categorization are welcome. 16 | - Please check your spelling and grammar. 17 | 18 | Thank you for your suggestions! 19 | --------------------------------------------------------------------------------