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Other awesome lists can be found in this [list](https://github.com/sindresorhus/awesome). 4 | 5 | - If you want to contribute to this list, please read [Contributing Guidelines](https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md). 6 | 7 | - [Curated list of R tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataScienceR). 8 | 9 | - [Curated list of Python tutorials for Data Science, NLP and Machine Learning](https://github.com/ujjwalkarn/DataSciencePython). 10 | 11 | 12 | ##Table of Contents 13 | - [Miscellaneous](#general) 14 | - [Interview Resources](#interview) 15 | - [Artificial Intelligence](#ai) 16 | - [Genetic Algorithms](#ga) 17 | - [Statistics](#stat) 18 | - [Useful Blogs](#blogs) 19 | - [Resources on Quora](#quora) 20 | - [Resources on Kaggle](#kaggle) 21 | - [Cheat Sheets](#cs) 22 | - [Classification](#classification) 23 | - [Linear Regression](#linear) 24 | - [Logistic Regression](#logistic) 25 | - [Model Validation using Resampling](#validation) 26 | - [Cross Validation](#cross) 27 | - [Bootstraping](#boot) 28 | - [Deep Learning](#deep) 29 | - [Frameworks](#frame) 30 | - [Feed Forward Networks](#feed) 31 | - [Recurrent Neural Nets, LSTM, GRU](#rnn) 32 | - [Restricted Boltzmann Machine, DBNs](#rbm) 33 | - [Autoencoders](#auto) 34 | - [Convolutional Neural Nets](#cnn) 35 | - [Natural Language Processing](#nlp) 36 | - [Topic Modeling, LDA](#topic) 37 | - [Word2Vec](#word2vec) 38 | - [Computer Vision](#vision) 39 | - [Support Vector Machine](#svm) 40 | - [Reinforcement Learning](#rl) 41 | - [Decision Trees](#dt) 42 | - [Random Forest / Bagging](#rf) 43 | - [Boosting](#gbm) 44 | - [Ensembles](#ensem) 45 | - [Stacking Models](#stack) 46 | - [VC Dimension](#vc) 47 | - [Bayesian Machine Learning](#bayes) 48 | - [Semi Supervised Learning](#semi) 49 | - [Optimizations](#opt) 50 | - [Other Useful Tutorials](#other) 51 | 52 | 53 | ##Miscellaneous 54 | - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) 55 | - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning) 56 | - [A curated list of awesome data visualization libraries and resources.](https://github.com/fasouto/awesome-dataviz) 57 | - [An awesome Data Science repository to learn and apply for real world problems](https://github.com/okulbilisim/awesome-datascience) 58 | - [The Open Source Data Science Masters](http://datasciencemasters.org/) 59 | - [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning) 60 | - [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning) 61 | - [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) 62 | - [Differnce between Linearly Independent, Orthogonal, and Uncorrelated Variables](https://www.psych.umn.edu/faculty/waller/classes/FA2010/Readings/rodgers.pdf) 63 | - [List of Machine Learning Concepts](https://en.wikipedia.org/wiki/List_of_machine_learning_concepts) 64 | - [Slides on Several Machine Learning Topics](http://www.slideshare.net/pierluca.lanzi/presentations) 65 | - [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html) 66 | - [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) 67 | - [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/) 68 | - [Machine Learning mistakes to avoid](https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l) 69 | - [Statistical Machine Learning Course](http://www.stat.cmu.edu/~larry/=sml/) 70 | - [TheAnalyticsEdge edX Notes and Codes](https://github.com/pedrosan/TheAnalyticsEdge) 71 | - [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) 72 | 73 | 74 | ##Interview Resources 75 | - [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) 76 | - [How do I learn Machine Learning?](https://www.quora.com/How-do-I-learn-machine-learning-1) 77 | - [FAQs about Data Science Interviews](https://www.quora.com/topic/Data-Science-Interviews/faq) 78 | - [What are the key skills of a data scientist?](https://www.quora.com/What-are-the-key-skills-of-a-data-scientist) 79 | 80 | 81 | ##Artificial Intelligence 82 | - [Awesome Artificial Intelligence (GitHub Repo)](https://github.com/owainlewis/awesome-artificial-intelligence) 83 | - [edX course | Klein & Abbeel](https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info) 84 | - [Udacity Course | Norvig & Thrun](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271) 85 | - [TED talks on AI](http://www.ted.com/playlists/310/talks_on_artificial_intelligen) 86 | 87 | 88 | ##Genetic Algorithms 89 | - [Genetic Algorithms Wikipedia Page](https://en.wikipedia.org/wiki/Genetic_algorithm) 90 | - [Simple Implementation of Genetic Algorithms in Python (Part 1)](http://outlace.com/Simple-Genetic-Algorithm-in-15-lines-of-Python/), [Part 2](http://outlace.com/Simple-Genetic-Algorithm-Python-Addendum/) 91 | - [Genetic Algorithms vs Artificial Neural Networks](http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks) 92 | - [Genetic Algorithms Explained in Plain English](http://www.ai-junkie.com/ga/intro/gat1.html) 93 | - [Genetic Programming](https://en.wikipedia.org/wiki/Genetic_programming) 94 | - [Genetic Programming in Python (GitHub)](https://github.com/trevorstephens/gplearn) 95 | - [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) 96 | 97 | 98 | ##Statistics 99 | - [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics 100 | - [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach 101 | - [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas 102 | - [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics 103 | - [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx) 104 | - Tutorials 105 | - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx) 106 | - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx) 107 | - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx) 108 | - [What is an Unbiased Estimator?](https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/) 109 | - [Goodness of Fit Explained](https://en.wikipedia.org/wiki/Goodness_of_fit) 110 | - [What are QQ Plots?](http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html) 111 | - [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook 112 | 113 | 114 | ##Useful Blogs 115 | - [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science 116 | - [The Data School Blog](http://www.dataschool.io/) - Data science for beginners! 117 | - [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning 118 | - [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general 119 | - [Colah's Blog](http://colah.github.io/) - Awesome Neural Networks Blog 120 | - [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering 121 | - [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog 122 | - [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors 123 | - [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond 124 | - [fastML](http://fastml.com/) - Machine learning made easy 125 | - [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page 126 | - [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science 127 | - [A Quantitative Journey | outlace](http://outlace.com/) - learning quantitative applications 128 | - [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R 129 | - [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog 130 | - [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence 131 | - [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/)- Making deep learning accessible 132 | 133 | 134 | ##Resources on Quora 135 | - [Most Viewed Machine Learning writers](https://www.quora.com/topic/Machine-Learning/writers) 136 | - [Data Science Topic on Quora](https://www.quora.com/Data-Science) 137 | - [William Chen's Answers](https://www.quora.com/William-Chen-6/answers) 138 | - [Michael Hochster's Answers](https://www.quora.com/Michael-Hochster/answers) 139 | - [Ricardo Vladimiro's Answers](https://www.quora.com/Ricardo-Vladimiro-1/answers) 140 | - [Storytelling with Statistics](https://datastories.quora.com/) 141 | - [Data Science FAQs on Quora](https://www.quora.com/topic/Data-Science/faq) 142 | - [Machine Learning FAQs on Quora](https://www.quora.com/topic/Machine-Learning/faq) 143 | 144 | 145 | ##Kaggle Competitions WriteUp 146 | - [How to almost win Kaggle Competitions](https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/) 147 | - [Convolution Neural Networks for EEG detection](http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/) 148 | - [Facebook Recruiting III Explained](http://alexminnaar.com/tag/kaggle-competitions.html) 149 | - [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/) 150 | - [How to Rank 10% in Your First Kaggle Competition](https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/) 151 | 152 | 153 | ##Cheat Sheets 154 | - [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf), [Source](http://www.wzchen.com/probability-cheatsheet/) 155 | - [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet) 156 | 157 | 158 | ##Classification 159 | - [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/) 160 | - [What is Deviance?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) 161 | - [When to choose which machine learning classifier?](http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier) 162 | - [What are the advantages of different classification algorithms?](https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms) 163 | - [ROC and AUC Explained](http://www.dataschool.io/roc-curves-and-auc-explained/) ([related video](https://youtu.be/OAl6eAyP-yo)) 164 | - [An introduction to ROC analysis](https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf) 165 | - [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) 166 | 167 | 168 | 169 | ##Linear Regression 170 | - [General](#general-) 171 | - [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) 172 | - [Linear Regression Comprehensive Resource](http://people.duke.edu/~rnau/regintro.htm) 173 | - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) 174 | - [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) 175 | - [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) 176 | - [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) 177 | - Multicollinearity and VIF 178 | - [Dummy Variable Trap | Multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity) 179 | - [Dealing with multicollinearity using VIFs](https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/) 180 | 181 | - [Residual Analysis](#residuals-) 182 | - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm) 183 | - [How to interpret a QQ plot?](http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1) 184 | - [Interpreting Residuals vs Fitted Plot](http://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions) 185 | 186 | - [Outliers](#outliers-) 187 | - [How should outliers be dealt with?](http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis) 188 | 189 | - [Elastic Net](https://en.wikipedia.org/wiki/Elastic_net_regularization) 190 | - [Regularization and Variable Selection via the 191 | Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf) 192 | 193 | 194 | ##Logistic Regression 195 | - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) 196 | - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) 197 | - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) 198 | - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) 199 | - [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) 200 | - [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) 201 | - [Guide to an in-depth understanding of logistic regression](http://www.dataschool.io/guide-to-logistic-regression/) 202 | 203 | 204 | ##Model Validation using Resampling 205 | 206 | - [Resampling Explained](https://en.wikipedia.org/wiki/Resampling_(statistics)) 207 | - [Partioning data set in R](http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations) 208 | - [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) 209 | 210 | 211 | - [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) 212 | - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) 213 | - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) 214 | - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation) 215 | - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set) 216 | - [Choice of k in k-fold CV](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation) 217 | - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning) 218 | - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret) 219 | - [Good Resources](http://www.chioka.in/tag/cross-validation/) 220 | - Overfitting and Cross Validation 221 | - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf) 222 | - [Over-fitting in Model Selection and Subsequent Selection Bias in 223 | Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf) 224 | - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf) 225 | - [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem) 226 | 227 | 228 | 229 | 230 | - [Bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) 231 | - [Why Bootstrapping Works?](http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works) 232 | - [Good Animation](https://www.stat.auckland.ac.nz/~wild/BootAnim/) 233 | - [Example of Bootstapping](http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm) 234 | - [Understanding Bootstapping for Validation and Model Selection](http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1) 235 | - [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) 236 | 237 | 238 | 239 | ##Deep Learning 240 | - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning) 241 | - [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html) 242 | - [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) 243 | - [Core Concepts of Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) 244 | - [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 245 | - [Stanford Deep Learning Tutorial](http://ufldl.stanford.edu/tutorial/) 246 | - [Deep Learning FAQs on Quora](https://www.quora.com/topic/Deep-Learning/faq) 247 | - [Google+ Deep Learning Page](https://plus.google.com/communities/112866381580457264725) 248 | - [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/) 249 | - [Where to Learn Deep Learning?](http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html) 250 | - [Deep Learning nvidia concepts](http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) 251 | - [Introduction to Deep Learning Using Python (GitHub)](https://github.com/rouseguy/intro2deeplearning), [Good Introduction Slides](https://speakerdeck.com/bargava/introduction-to-deep-learning) 252 | - [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](http://videolectures.net/deeplearning2015_montreal/) 253 | - [Deep Learning Software List](http://deeplearning.net/software_links/) 254 | - [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/) 255 | - [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html) 256 | - [Geoff Hinton Youtube Vidoes on Deep Learning](https://www.youtube.com/watch?v=IcOMKXAw5VA) 257 | - [Awesome Deep Learning Reading List](http://deeplearning.net/reading-list/) 258 | - [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/) 259 | - [deeplearning Tutorials](http://deeplearning4j.org/) 260 | - [AWESOME! Deep Learning Tutorial](https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) 261 | - [Deep Learning Basics](http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html) 262 | - [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/) 263 | - [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) 264 | - [Artificial Neural Networks Tutorials](http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks) 265 | - [Neural Networks FAQs on Stack Overflow](http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50) 266 | - [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html) 267 | - [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/) 268 | 269 | - Neural Machine Translation 270 | - [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/) 271 | - [Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning](https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/) 272 | 273 | 274 | - Deep Learning Frameworks 275 | - [Torch vs. Theano](http://fastml.com/torch-vs-theano/) 276 | - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) 277 | - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/) 278 | 279 | - [Theano](https://en.wikipedia.org/wiki/Theano_(software)) 280 | - [Website](http://deeplearning.net/software/theano/) 281 | - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/) 282 | - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/) 283 | - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/) 284 | - [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg) 285 | - [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp) 286 | - [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet) 287 | - [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu) 288 | - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) 289 | - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm) 290 | - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn) 291 | - [All Codes](https://github.com/lisa-lab/DeepLearningTutorials) 292 | - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/) 293 | 294 | - [Torch](http://torch.ch/) 295 | - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials) 296 | - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf) 297 | - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch) 298 | - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch) 299 | - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015) 300 | - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html) 301 | - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet) 302 | - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 303 | 304 | - Caffe 305 | - [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/) 306 | 307 | - TensorFlow 308 | - [Website](http://tensorflow.org/) 309 | - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples) 310 | - [Simplified Scikit-learn Style Interface to TensorFlow](https://github.com/tensorflow/skflow) 311 | - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow) 312 | - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66) 313 | - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow) 314 | 315 | 316 | 317 | - Feed Forward Networks 318 | - [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) 319 | - [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) 320 | - [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) 321 | - [Basic ANN Theory](https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/) 322 | - [Role of Bias in Neural Networks](http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks) 323 | - [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#) 324 | - [Backpropagation in Matrix Form](http://sudeepraja.github.io/Neural/) 325 | - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html) 326 | - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation) 327 | - [NN for Beginners](http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of) 328 | - [Regression and Classification with NNs (Slides)](http://www.autonlab.org/tutorials/neural13.pdf) 329 | - [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html) 330 | 331 | 332 | - Recurrent and LSTM Networks 333 | - [awesome-rnn: list of resources (GitHub Repo)](https://github.com/kjw0612/awesome-rnn) 334 | - [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/) 335 | - [NLP RNN Representations](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) 336 | - [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) 337 | - [Intro to RNN](http://deeplearning4j.org/recurrentnetwork.html), [LSTM](http://deeplearning4j.org/lstm.html) 338 | - [An application of RNN](http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/) 339 | - [Optimizing RNN Performance](http://svail.github.io/) 340 | - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/) 341 | - [Auto-Generating Clickbait with RNN](https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/) 342 | - [Sequence Learning using RNN (Slides)](http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml) 343 | - [Machine Translation using RNN (Paper)](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf) 344 | - [Music generation using RNNs (Keras)](https://github.com/MattVitelli/GRUV) 345 | - [Using RNN to create on-the-fly dialogue (Keras)](http://neuralniche.com/post/tutorial/) 346 | - Long Short Term Memory (LSTM) 347 | - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) 348 | - [LSTM explained](https://apaszke.github.io/lstm-explained.html) 349 | - [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html) 350 | - [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) 351 | - [Torch Code for character-level language models using LSTM](https://github.com/karpathy/char-rnn) 352 | - [LSTM for Kaggle EEG Detection competition (Torch Code)](https://github.com/apaszke/kaggle-grasp-and-lift) 353 | - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm) 354 | - [Deep Learning for Visual Q&A | LSTM | CNN](http://avisingh599.github.io/deeplearning/visual-qa/), [Code](https://github.com/avisingh599/visual-qa) 355 | - [Computer Responds to email using LSTM | Google](http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html) 356 | - [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/) 357 | - [Understanding Natural Language with LSTM Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 358 | - [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neural-vqa) 359 | - Gated Recurrent Units (GRU) 360 | - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/) 361 | 362 | 363 | 364 | - [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network) 365 | - [Recursive Neural Tensor Network (RNTN)](http://deeplearning4j.org/recursiveneuraltensornetwork.html) 366 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 367 | 368 | 369 | - Restricted Boltzmann Machine 370 | - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html) 371 | - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html) 372 | - [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/) 373 | - [Hinton's Guide to Training RBMs](https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) 374 | - [RBMs in R](https://github.com/zachmayer/rbm) 375 | - [Deep Belief Networks Tutorial](http://deeplearning4j.org/deepbeliefnetwork.html) 376 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 377 | 378 | 379 | - Autoencoders: Unsupervised (applies BackProp after setting target = input) 380 | - [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) 381 | - [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html) 382 | - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py) 383 | - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda) 384 | 385 | 386 | 387 | - Convolutional Neural Networks 388 | - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) 389 | - [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision) 390 | - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html) 391 | - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) 392 | - [Stanford Notes](http://vision.stanford.edu/teaching/cs231n/), [Codes](http://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io) 393 | - [JavaScript Library (Browser Based) for CNNs](http://cs.stanford.edu/people/karpathy/convnetjs/) 394 | - [Using CNNs to detect facial keypoints](http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/) 395 | - [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) 396 | - [Interview with Yann LeCun | Kaggle](http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/) 397 | - [Visualising and Understanding CNNs](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) 398 | 399 | 400 | 401 | ##Natural Language Processing 402 | - [A curated list of speech and natural language processing resources](https://github.com/edobashira/speech-language-processing) 403 | - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 404 | - [tf-idf explained](http://michaelerasm.us/tf-idf-in-10-minutes/) 405 | - [Interesting Deep Learning NLP Projects Stanford](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/) 406 | - [NLP from Scratch | Google Paper](https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf) 407 | - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) 408 | - [Bag of Words](https://en.wikipedia.org/wiki/Bag-of-words_model) 409 | - [Classification text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) 410 | 411 | - [Topic Modeling](https://en.wikipedia.org/wiki/Topic_model) 412 | - [LDA](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) 413 | - [Awesome LDA Explanation!](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) 414 | - [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/) 415 | - [Difference between LSI and LDA](https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA) 416 | - [Original LDA Paper](https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf) 417 | - [alpha and beta in LDA](http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a) 418 | - [Intuitive explanation of the Dirichlet distribution](https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution) 419 | - [Topic modeling made just simple enough](https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/) 420 | - [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) 421 | - [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) 422 | - [Segmentation of Twitter Timelines via Topic Modeling](http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html) 423 | - [Topic Modeling of Twitter Followers](http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html) 424 | 425 | 426 | - word2vec 427 | - [Google word2vec](https://code.google.com/archive/p/word2vec) 428 | - [Bag of Words Model Wiki](https://en.wikipedia.org/wiki/Bag-of-words_model) 429 | - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) 430 | - [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) 431 | - [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) 432 | - [Making sense of word2vec](http://rare-technologies.com/making-sense-of-word2vec/) 433 | - [word2vec explained on deeplearning4j](http://deeplearning4j.org/word2vec.html) 434 | - [Quora word2vec](https://www.quora.com/How-does-word2vec-work) 435 | - [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) 436 | - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html) 437 | 438 | - Text Clustering 439 | - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering) 440 | - [Levenshtein distance for measuring the difference between two sequences](https://en.wikipedia.org/wiki/Levenshtein_distance) 441 | - [Text clustering with Levenshtein distances](http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances) 442 | 443 | - Text Classification 444 | - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/) 445 | 446 | - [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/) 447 | - [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) 448 | - [What would Shakespeare say (NLP Tutorial)](https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/) 449 | - [A closer look at Skip Gram Modeling](http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf) 450 | 451 | 452 | ##Computer Vision 453 | - [Awesome computer vision (github)](https://github.com/jbhuang0604/awesome-computer-vision) 454 | - [Awesome deep vision (github)](https://github.com/kjw0612/awesome-deep-vision) 455 | 456 | 457 | 458 | ##Support Vector Machine 459 | - [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm) 460 | - [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines) 461 | - [SVM in Layman's terms](https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms) 462 | - [How does SVM Work | Comparisons](http://stats.stackexchange.com/questions/23391/how-does-a-support-vector-machine-svm-work) 463 | - [A tutorial on SVMs](http://alex.smola.org/papers/2003/SmoSch03b.pdf) 464 | - [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) 465 | - [Introductory Overview of SVMs](http://www.statsoft.com/Textbook/Support-Vector-Machines) 466 | - Comparisons 467 | - [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) 468 | - [Trees > SVMs](http://stats.stackexchange.com/questions/57438/why-is-svm-not-so-good-as-decision-tree-on-the-same-data) 469 | - [Kernel Logistic Regression vs SVM](http://stats.stackexchange.com/questions/43996/kernel-logistic-regression-vs-svm) 470 | - [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) 471 | - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) 472 | - [Variable Importance from SVM](http://stats.stackexchange.com/questions/2179/variable-importance-from-svm) 473 | - Software 474 | - [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) 475 | - [Intro to SVM in R](http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf) 476 | - Kernels 477 | - [What are Kernels in ML and SVM?](https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM) 478 | - [Intuition Behind Gaussian Kernel in SVMs?](https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM) 479 | - Probabilities post SVM 480 | - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf) 481 | - [Platt Calibration Wiki](https://en.wikipedia.org/wiki/Platt_scaling) 482 | - [Why use Platts Scaling](http://stats.stackexchange.com/questions/5196/why-use-platts-scaling) 483 | - [Classifier Classification with Platt's Scaling](http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/) 484 | 485 | 486 | 487 | ##Reinforcement Learning 488 | - [Awesome Reinforcement Learning (GitHub)](https://github.com/aikorea/awesome-rl) 489 | - [RL Tutorial Part 1](http://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](http://outlace.com/Reinforcement-Learning-Part-2/) 490 | 491 | 492 | ##Decision Trees 493 | - [Wikipedia Page - Lots of Good Info](https://en.wikipedia.org/wiki/Decision_tree_learning) 494 | - [FAQs about Decision Trees](http://stats.stackexchange.com/questions/tagged/cart) 495 | - [Brief Tour of Trees and Forests](http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/) 496 | - [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html) 497 | - [How Decision Trees work?](http://www.aihorizon.com/essays/generalai/decision_trees.htm) 498 | - [Weak side of Decision Trees](http://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees) 499 | - [Thorough Explanation and different algorithms](http://www.ise.bgu.ac.il/faculty/liorr/hbchap9.pdf) 500 | - [What is entropy and information gain in the context of building decision trees?](http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain) 501 | - [Slides Related to Decision Trees](http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees) 502 | - [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) 503 | - [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) 504 | - [Good Article](https://www.mindtools.com/dectree.html) 505 | - [Are decision trees almost always binary trees?](http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees) 506 | - [Pruning Decision Trees](https://en.wikipedia.org/wiki/Pruning_(decision_trees)), [Grafting of Decision Trees](https://en.wikipedia.org/wiki/Grafting_(decision_trees)) 507 | - [What is Deviance in context of Decision Trees?](http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart) 508 | - Comparison of Different Algorithms 509 | - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees) 510 | - [Comparison of complexity or performance](https://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance) 511 | - [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) 512 | - [Good Article on comparison](http://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11) 513 | - CART 514 | - [Recursive Partitioning Wikipedia](https://en.wikipedia.org/wiki/Recursive_partitioning) 515 | - [CART Explained](http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees) 516 | - [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) 517 | - [Pruning a Tree in R](http://stackoverflow.com/questions/15318409/how-to-prune-a-tree-in-r) 518 | - [Does rpart use multivariate splits by default?](http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default) 519 | - [FAQs about Recursive Partitioning](http://stats.stackexchange.com/questions/tagged/rpart) 520 | - CTREE 521 | - [party package in R](https://cran.r-project.org/web/packages/party/party.pdf) 522 | - [Show volumne in each node using ctree in R](http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r) 523 | - [How to extract tree structure from ctree function?](http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function) 524 | - CHAID 525 | - [Wikipedia Artice on CHAID](https://en.wikipedia.org/wiki/CHAID) 526 | - [Basic Introduction to CHAID](https://smartdrill.com/Introduction-to-CHAID.html) 527 | - [Good Tutorial on CHAID](http://www.statsoft.com/Textbook/CHAID-Analysis) 528 | - MARS 529 | - [Wikipedia Article on MARS](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines) 530 | - Probabilistic Decision Trees 531 | - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf) 532 | - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf) 533 | 534 | 535 | ##Random Forest / Bagging 536 | - [Awesome Random Forest (GitHub)**](https://github.com/kjw0612/awesome-random-forest) 537 | - [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) 538 | - [Measures of variable importance in random forests](http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests) 539 | - [Compare R-squared from two different Random Forest models](http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models) 540 | - [OOB Estimate Explained | RF vs LDA](https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf) 541 | - [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve](https://www.jstatsoft.org/index.php/jss/article/view/v050i11) 542 | - [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) 543 | - [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) 544 | - [FAQs about Random Forest](http://stats.stackexchange.com/questions/tagged/random-forest), [More FAQs](http://stackoverflow.com/questions/tagged/random-forest) 545 | - [Obtaining knowledge from a random forest](http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest) 546 | - [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) 547 | 548 | 549 | ##Boosting 550 | - [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions) 551 | - [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning)) 552 | - [Introduction to Boosted Trees | Tianqi Chen](https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf) 553 | - Gradient Boosting Machine 554 | - [Gradiet Boosting Wiki](https://en.wikipedia.org/wiki/Gradient_boosting) 555 | - [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) 556 | - [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) 557 | - [Role of n.minobsinnode parameter of GBM in R](http://stats.stackexchange.com/questions/30645/role-of-n-minobsinnode-parameter-of-gbm-in-r) 558 | - [GBM in R](http://www.slideshare.net/mark_landry/gbm-package-in-r) 559 | - [FAQs about GBM](http://stats.stackexchange.com/tags/gbm/hot) 560 | - [GBM vs xgboost](https://www.kaggle.com/c/higgs-boson/forums/t/9497/r-s-gbm-vs-python-s-xgboost) 561 | 562 | - xgboost 563 | - [xgboost tuning kaggle](https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log) 564 | - [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) 565 | - [xgboost survey](https://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey) 566 | - [Practical XGBoost in Python online course (free)](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) 567 | - AdaBoost 568 | - [AdaBoost Wiki](https://en.wikipedia.org/wiki/AdaBoost), [Python Code](https://gist.github.com/tristanwietsma/5486024) 569 | - [AdaBoost Sparse Input Support](http://hamzehal.blogspot.com/2014/06/adaboost-sparse-input-support.html) 570 | - [adaBag R package](https://cran.r-project.org/web/packages/adabag/adabag.pdf) 571 | - [Tutorial](http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf) 572 | 573 | 574 | ##Ensembles 575 | - [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning) 576 | - [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) 577 | - [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/) 578 | - [Ensemble Learning Intro](http://machine-learning.martinsewell.com/ensembles/) 579 | - [Ensemble Learning Paper](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf) 580 | - [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/) 581 | - [Ensembling Models with caret](http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret) 582 | - [Bagging vs Boosting vs Stacking](http://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning) 583 | - [Good Resources | Kaggle Africa Soil Property Prediction](https://www.kaggle.com/c/afsis-soil-properties/forums/t/10391/best-ensemble-references) 584 | - [Boosting vs Bagging](http://www.chioka.in/which-is-better-boosting-or-bagging/) 585 | - [Resources for learning how to implement ensemble methods](http://stats.stackexchange.com/questions/32703/resources-for-learning-how-to-implement-ensemble-methods) 586 | - [How are classifications merged in an ensemble classifier?](http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier) 587 | 588 | 589 | ##Stacking Models 590 | - [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/) 591 | - [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/) 592 | - [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf) 593 | - [Stacked Generalization Paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf) 594 | 595 | 596 | ##Vapnik–Chervonenkis Dimension 597 | - [Wikipedia article on VC Dimension](https://en.wikipedia.org/wiki/VC_dimension) 598 | - [Intuitive Explanantion of VC Dimension](https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way) 599 | - [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c) 600 | - [Introduction to VC Dimension](http://www.svms.org/vc-dimension/) 601 | - [FAQs about VC Dimension](http://stats.stackexchange.com/questions/tagged/vc-dimension) 602 | - [Do ensemble techniques increase VC-dimension?](http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension) 603 | 604 | 605 | 606 | ##Bayesian Machine Learning 607 | - [Bayesian Methods for Hackers (using pyMC)](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) 608 | - [Should all Machine Learning be Bayesian?](http://videolectures.net/bark08_ghahramani_samlbb/) 609 | - [Tutorial on Bayesian Optimisation for Machine Learning](http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf) 610 | - [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) 611 | - [Bayesian Statistics Made Simple](http://greenteapress.com/wp/think-bayes/) 612 | - [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) 613 | - [Markov Chain Wikipedia Page](https://en.wikipedia.org/wiki/Markov_chain) 614 | 615 | 616 | 617 | ##Semi Supervised Learning 618 | - [Wikipedia article on Semi Supervised Learning](https://en.wikipedia.org/wiki/Semi-supervised_learning) 619 | - [Tutorial on Semi Supervised Learning](http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf) 620 | - [Graph Based Semi Supervised Learning for NLP](http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf) 621 | - [Taxonomy](http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].pdf) 622 | - [Video Tutorial Weka](https://www.youtube.com/watch?v=sWxcIjZFGNM) 623 | - [Unsupervised, Supervised and Semi Supervised learning](http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning) 624 | - [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) 625 | 626 | 627 | 628 | 629 | ##Optimization 630 | - [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) 631 | - [Algorithms for Sparse Optimization and Machine 632 | Learning](http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12) 633 | - [Optimization Algorithms in Machine Learning](http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf), [Video Lecture](http://videolectures.net/nips2010_wright_oaml/) 634 | - [Optimization Algorithms for Data Analysis](http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf) 635 | - [Video Lectures on Optimization](http://videolectures.net/stephen_j_wright/) 636 | - [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf) 637 | - [The Interplay of Optimization and Machine Learning Research](http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf) 638 | 639 | 640 | ##Other Tutorials 641 | - For a collection of Data Science Tutorials using R, please refer to [this list](https://github.com/ujjwalkarn/DataScienceR). 642 | - For a collection of Data Science Tutorials using Python, please refer to [this list](https://github.com/ujjwalkarn/DataSciencePython). 643 | -------------------------------------------------------------------------------- /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 | - The pull request and commit should have a useful title. 9 | - Please search previous suggestions before making a new one, as yours may be a duplicate. 10 | - Make sure your link has a useful and relevant title. 11 | - Please use [title-casing](http://titlecapitalization.com) (AP style). 12 | - Please use the following format: `[Useful Title](link)` 13 | - Link additions should be added to the bottom of the relevant category. 14 | - New categories or improvements to the existing categorization are welcome. 15 | - Please check your spelling and grammar. 16 | 17 | Thank you for your suggestions! 18 | --------------------------------------------------------------------------------