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| https://github.com/sindresorhus/awesome |
| https://github.com/Programmer027/Machine-Learning-Tutorials#machine-learning--deep-learning-tutorials- |
| list | https://github.com/sindresorhus/awesome |
| Contributing Guidelines | https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/contributing.md |
| Curated list of R tutorials for Data Science, NLP and Machine Learning | https://github.com/ujjwalkarn/DataScienceR |
| Curated list of Python tutorials for Data Science, NLP and Machine Learning | https://github.com/ujjwalkarn/DataSciencePython |
| https://github.com/Programmer027/Machine-Learning-Tutorials#contents |
| Introduction | https://github.com/Programmer027/Machine-Learning-Tutorials#general |
| Interview Resources | https://github.com/Programmer027/Machine-Learning-Tutorials#interview |
| Artificial Intelligence | https://github.com/Programmer027/Machine-Learning-Tutorials#ai |
| Genetic Algorithms | https://github.com/Programmer027/Machine-Learning-Tutorials#ga |
| Statistics | https://github.com/Programmer027/Machine-Learning-Tutorials#stat |
| Useful Blogs | https://github.com/Programmer027/Machine-Learning-Tutorials#blogs |
| Resources on Quora | https://github.com/Programmer027/Machine-Learning-Tutorials#quora |
| Resources on Kaggle | https://github.com/Programmer027/Machine-Learning-Tutorials#kaggle |
| Cheat Sheets | https://github.com/Programmer027/Machine-Learning-Tutorials#cs |
| Classification | https://github.com/Programmer027/Machine-Learning-Tutorials#classification |
| Linear Regression | https://github.com/Programmer027/Machine-Learning-Tutorials#linear |
| Logistic Regression | https://github.com/Programmer027/Machine-Learning-Tutorials#logistic |
| Model Validation using Resampling | https://github.com/Programmer027/Machine-Learning-Tutorials#validation |
| Cross Validation | https://github.com/Programmer027/Machine-Learning-Tutorials#cross |
| Bootstraping | https://github.com/Programmer027/Machine-Learning-Tutorials#boot |
| Deep Learning | https://github.com/Programmer027/Machine-Learning-Tutorials#deep |
| Frameworks | https://github.com/Programmer027/Machine-Learning-Tutorials#frame |
| Feed Forward Networks | https://github.com/Programmer027/Machine-Learning-Tutorials#feed |
| Recurrent Neural Nets, LSTM, GRU | https://github.com/Programmer027/Machine-Learning-Tutorials#rnn |
| Restricted Boltzmann Machine, DBNs | https://github.com/Programmer027/Machine-Learning-Tutorials#rbm |
| Autoencoders | https://github.com/Programmer027/Machine-Learning-Tutorials#auto |
| Convolutional Neural Nets | https://github.com/Programmer027/Machine-Learning-Tutorials#cnn |
| Natural Language Processing | https://github.com/Programmer027/Machine-Learning-Tutorials#nlp |
| Topic Modeling, LDA | https://github.com/Programmer027/Machine-Learning-Tutorials#topic |
| Word2Vec | https://github.com/Programmer027/Machine-Learning-Tutorials#word2vec |
| Computer Vision | https://github.com/Programmer027/Machine-Learning-Tutorials#vision |
| Support Vector Machine | https://github.com/Programmer027/Machine-Learning-Tutorials#svm |
| Reinforcement Learning | https://github.com/Programmer027/Machine-Learning-Tutorials#rl |
| Decision Trees | https://github.com/Programmer027/Machine-Learning-Tutorials#dt |
| Random Forest / Bagging | https://github.com/Programmer027/Machine-Learning-Tutorials#rf |
| Boosting | https://github.com/Programmer027/Machine-Learning-Tutorials#gbm |
| Ensembles | https://github.com/Programmer027/Machine-Learning-Tutorials#ensem |
| Stacking Models | https://github.com/Programmer027/Machine-Learning-Tutorials#stack |
| VC Dimension | https://github.com/Programmer027/Machine-Learning-Tutorials#vc |
| Bayesian Machine Learning | https://github.com/Programmer027/Machine-Learning-Tutorials#bayes |
| Semi Supervised Learning | https://github.com/Programmer027/Machine-Learning-Tutorials#semi |
| Optimizations | https://github.com/Programmer027/Machine-Learning-Tutorials#opt |
| Other Useful Tutorials | https://github.com/Programmer027/Machine-Learning-Tutorials#other |
| https://github.com/Programmer027/Machine-Learning-Tutorials#introduction |
| Machine Learning Course by Andrew Ng (Stanford University) | https://www.coursera.org/learn/machine-learning |
| In-depth introduction to machine learning in 15 hours of expert videos | http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ |
| An Introduction to Statistical Learning | http://www-bcf.usc.edu/~gareth/ISL/ |
| List of Machine Learning University Courses | https://github.com/prakhar1989/awesome-courses#machine-learning |
| Machine Learning for Software Engineers | https://github.com/ZuzooVn/machine-learning-for-software-engineers |
| Dive into Machine Learning | https://github.com/hangtwenty/dive-into-machine-learning |
| A curated list of awesome Machine Learning frameworks, libraries and software | https://github.com/josephmisiti/awesome-machine-learning |
| A curated list of awesome data visualization libraries and resources. | https://github.com/fasouto/awesome-dataviz |
| An awesome Data Science repository to learn and apply for real world problems | https://github.com/okulbilisim/awesome-datascience |
| The Open Source Data Science Masters | http://datasciencemasters.org/ |
| Machine Learning FAQs on Cross Validated | http://stats.stackexchange.com/questions/tagged/machine-learning |
| Machine Learning algorithms that you should always have a strong understanding of | https://www.quora.com/What-are-some-Machine-Learning-algorithms-that-you-should-always-have-a-strong-understanding-of-and-why |
| Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables | http://terpconnect.umd.edu/~bmomen/BIOM621/LineardepCorrOrthogonal.pdf |
| List of Machine Learning Concepts | https://en.wikipedia.org/wiki/List_of_machine_learning_concepts |
| Slides on Several Machine Learning Topics | http://www.slideshare.net/pierluca.lanzi/presentations |
| MIT Machine Learning Lecture Slides | http://www.ai.mit.edu/courses/6.867-f04/lectures.html |
| Comparison Supervised Learning Algorithms | http://www.dataschool.io/comparing-supervised-learning-algorithms/ |
| Learning Data Science Fundamentals | http://www.dataschool.io/learning-data-science-fundamentals/ |
| Machine Learning mistakes to avoid | https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4#.lih061l3l |
| Statistical Machine Learning Course | http://www.stat.cmu.edu/~larry/=sml/ |
| TheAnalyticsEdge edX Notes and Codes | https://github.com/pedrosan/TheAnalyticsEdge |
| Have Fun With Machine Learning | https://github.com/humphd/have-fun-with-machine-learning |
| Twitter's Most Shared #machineLearning Content From The Past 7 Days | http://theherdlocker.com/tweet/popularity/machinelearning |
| https://github.com/Programmer027/Machine-Learning-Tutorials#interview-resources |
| 41 Essential Machine Learning Interview Questions (with answers) | https://www.springboard.com/blog/machine-learning-interview-questions/ |
| How can a computer science graduate student prepare himself for data scientist interviews? | https://www.quora.com/How-can-a-computer-science-graduate-student-prepare-himself-for-data-scientist-machine-learning-intern-interviews |
| How do I learn Machine Learning? | https://www.quora.com/How-do-I-learn-machine-learning-1 |
| FAQs about Data Science Interviews | https://www.quora.com/topic/Data-Science-Interviews/faq |
| What are the key skills of a data scientist? | https://www.quora.com/What-are-the-key-skills-of-a-data-scientist |
| https://github.com/Programmer027/Machine-Learning-Tutorials#artificial-intelligence |
| Awesome Artificial Intelligence (GitHub Repo) | https://github.com/owainlewis/awesome-artificial-intelligence |
| UC Berkeley CS188 Intro to AI | http://ai.berkeley.edu/home.html |
| Lecture Videos | http://ai.berkeley.edu/lecture_videos.html |
| 2 | https://www.youtube.com/watch?v=W1S-HSakPTM |
| 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/ |
| edX course | Klein & Abbeel | https://courses.edx.org/courses/BerkeleyX/CS188x_1/1T2013/info |
| Udacity Course | Norvig & Thrun | https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 |
| TED talks on AI | http://www.ted.com/playlists/310/talks_on_artificial_intelligen |
| https://github.com/Programmer027/Machine-Learning-Tutorials#genetic-algorithms |
| Genetic Algorithms Wikipedia Page | https://en.wikipedia.org/wiki/Genetic_algorithm |
| Simple Implementation of Genetic Algorithms in Python (Part 1) | http://outlace.com/miniga.html |
| Part 2 | http://outlace.com/miniga_addendum.html |
| Genetic Algorithms vs Artificial Neural Networks | http://stackoverflow.com/questions/1402370/when-to-use-genetic-algorithms-vs-when-to-use-neural-networks |
| Genetic Algorithms Explained in Plain English | http://www.ai-junkie.com/ga/intro/gat1.html |
| Genetic Programming | https://en.wikipedia.org/wiki/Genetic_programming |
| Genetic Programming in Python (GitHub) | https://github.com/trevorstephens/gplearn |
| 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 |
| https://github.com/Programmer027/Machine-Learning-Tutorials#statistics |
| Stat Trek Website | http://stattrek.com/ |
| Learn Statistics Using Python | https://github.com/rouseguy/intro2stats |
| Statistics for Hackers | Slides | @jakevdp | https://speakerdeck.com/jakevdp/statistics-for-hackers |
| Online Statistics Book | http://onlinestatbook.com/2/index.html |
| What is a Sampling Distribution? | http://stattrek.com/sampling/sampling-distribution.aspx |
| AP Statistics Tutorial | http://stattrek.com/tutorials/ap-statistics-tutorial.aspx |
| Statistics and Probability Tutorial | http://stattrek.com/tutorials/statistics-tutorial.aspx |
| Matrix Algebra Tutorial | http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx |
| What is an Unbiased Estimator? | https://www.physicsforums.com/threads/what-is-an-unbiased-estimator.547728/ |
| Goodness of Fit Explained | https://en.wikipedia.org/wiki/Goodness_of_fit |
| What are QQ Plots? | http://onlinestatbook.com/2/advanced_graphs/q-q_plots.html |
| OpenIntro Statistics | https://www.openintro.org/stat/textbook.php?stat_book=os |
| https://github.com/Programmer027/Machine-Learning-Tutorials#useful-blogs |
| Edwin Chen's Blog | http://blog.echen.me/ |
| The Data School Blog | http://www.dataschool.io/ |
| ML Wave | http://mlwave.com/ |
| Andrej Karpathy | http://karpathy.github.io/ |
| Colah's Blog | http://colah.github.io/ |
| Alex Minnaar's Blog | http://alexminnaar.com/ |
| Statistically Significant | http://andland.github.io/ |
| Simply Statistics | http://simplystatistics.org/ |
| Yanir Seroussi's Blog | https://yanirseroussi.com/ |
| fastML | http://fastml.com/ |
| Trevor Stephens Blog | http://trevorstephens.com/ |
| no free hunch | kaggle | http://blog.kaggle.com/ |
| A Quantitative Journey | outlace | http://outlace.com/ |
| r4stats | http://r4stats.com/ |
| Variance Explained | http://varianceexplained.org/ |
| AI Junkie | http://www.ai-junkie.com/ |
| Deep Learning Blog by Tim Dettmers | http://timdettmers.com/ |
| J Alammar's Blog | http://jalammar.github.io/ |
| Adam Geitgey | https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne |
| Ethen's Notebook Collection | https://github.com/ethen8181/machine-learning |
| https://github.com/Programmer027/Machine-Learning-Tutorials#resources-on-quora |
| Most Viewed Machine Learning writers | https://www.quora.com/topic/Machine-Learning/writers |
| Data Science Topic on Quora | https://www.quora.com/Data-Science |
| William Chen's Answers | https://www.quora.com/William-Chen-6/answers |
| Michael Hochster's Answers | https://www.quora.com/Michael-Hochster/answers |
| Ricardo Vladimiro's Answers | https://www.quora.com/Ricardo-Vladimiro-1/answers |
| Storytelling with Statistics | https://datastories.quora.com/ |
| Data Science FAQs on Quora | https://www.quora.com/topic/Data-Science/faq |
| Machine Learning FAQs on Quora | https://www.quora.com/topic/Machine-Learning/faq |
| https://github.com/Programmer027/Machine-Learning-Tutorials#kaggle-competitions-writeup |
| How to almost win Kaggle Competitions | https://yanirseroussi.com/2014/08/24/how-to-almost-win-kaggle-competitions/ |
| Convolution Neural Networks for EEG detection | http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/ |
| Facebook Recruiting III Explained | http://alexminnaar.com/tag/kaggle-competitions.html |
| Predicting CTR with Online ML | http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/ |
| How to Rank 10% in Your First Kaggle Competition | https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#cheat-sheets |
| Probability Cheat Sheet | http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf |
| Source | http://www.wzchen.com/probability-cheatsheet/ |
| Machine Learning Cheat Sheet | https://github.com/soulmachine/machine-learning-cheat-sheet |
| https://github.com/Programmer027/Machine-Learning-Tutorials#classification |
| Does Balancing Classes Improve Classifier Performance? | http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/ |
| What is Deviance? | http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart |
| When to choose which machine learning classifier? | http://stackoverflow.com/questions/2595176/when-to-choose-which-machine-learning-classifier |
| What are the advantages of different classification algorithms? | https://www.quora.com/What-are-the-advantages-of-different-classification-algorithms |
| ROC and AUC Explained | http://www.dataschool.io/roc-curves-and-auc-explained/ |
| related video | https://youtu.be/OAl6eAyP-yo |
| An introduction to ROC analysis | https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf |
| Simple guide to confusion matrix terminology | http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#linear-regression |
| General | https://github.com/Programmer027/Machine-Learning-Tutorials#general- |
| Assumptions of Linear Regression | http://pareonline.net/getvn.asp?n=2&v=8 |
| Stack Exchange | http://stats.stackexchange.com/questions/16381/what-is-a-complete-list-of-the-usual-assumptions-for-linear-regression |
| Linear Regression Comprehensive Resource | http://people.duke.edu/~rnau/regintro.htm |
| Applying and Interpreting Linear Regression | http://www.dataschool.io/applying-and-interpreting-linear-regression/ |
| What does having constant variance in a linear regression model mean? | http://stats.stackexchange.com/questions/52089/what-does-having-constant-variance-in-a-linear-regression-model-mean/52107?stw=2#52107 |
| Difference between linear regression on y with x and x with y | http://stats.stackexchange.com/questions/22718/what-is-the-difference-between-linear-regression-on-y-with-x-and-x-with-y?lq=1 |
| Is linear regression valid when the dependant variable is not normally distributed? | https://www.researchgate.net/post/Is_linear_regression_valid_when_the_outcome_dependant_variable_not_normally_distributed |
| Dummy Variable Trap | Multicollinearity | https://en.wikipedia.org/wiki/Multicollinearity |
| Dealing with multicollinearity using VIFs | https://jonlefcheck.net/2012/12/28/dealing-with-multicollinearity-using-variance-inflation-factors/ |
| Residual Analysis | https://github.com/Programmer027/Machine-Learning-Tutorials#residuals- |
| Interpreting plot.lm() in R | http://stats.stackexchange.com/questions/58141/interpreting-plot-lm |
| How to interpret a QQ plot? | http://stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot?lq=1 |
| Interpreting Residuals vs Fitted Plot | http://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions |
| Outliers | https://github.com/Programmer027/Machine-Learning-Tutorials#outliers- |
| How should outliers be dealt with? | http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis |
| Elastic Net | https://en.wikipedia.org/wiki/Elastic_net_regularization |
| Regularization and Variable Selection via the
Elastic Net | https://web.stanford.edu/~hastie/Papers/elasticnet.pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#logistic-regression |
| Logistic Regression Wiki | https://en.wikipedia.org/wiki/Logistic_regression |
| Geometric Intuition of Logistic Regression | http://florianhartl.com/logistic-regression-geometric-intuition.html |
| Obtaining predicted categories (choosing threshold) | http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit |
| Residuals in logistic regression | http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean |
| Difference between logit and probit models | http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909 |
| Logistic Regression Wiki | https://en.wikipedia.org/wiki/Logistic_regression |
| Probit Model Wiki | https://en.wikipedia.org/wiki/Probit_model |
| Pseudo R2 for Logistic Regression | http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s |
| How to calculate | http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression |
| Other Details | http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm |
| Guide to an in-depth understanding of logistic regression | http://www.dataschool.io/guide-to-logistic-regression/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#model-validation-using-resampling |
| Resampling Explained | https://en.wikipedia.org/wiki/Resampling_(statistics) |
| Partioning data set in R | http://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations |
| Implementing hold-out Validaion in R | http://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r |
| 2 | http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-testing-and.html |
| Cross Validation | https://en.wikipedia.org/wiki/Cross-validation_(statistics) |
| How to use cross-validation in predictive modeling | http://stuartlacy.co.uk/2016/02/04/how-to-correctly-use-cross-validation-in-predictive-modelling/ |
| Training with Full dataset after CV? | http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation |
| Which CV method is best? | http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best |
| Variance Estimates in k-fold CV | http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation |
| Is CV a subsitute for Validation Set? | http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set |
| Choice of k in k-fold CV | http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation |
| CV for ensemble learning | http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning |
| k-fold CV in R | http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret |
| Good Resources | http://www.chioka.in/tag/cross-validation/ |
| Preventing Overfitting the Cross Validation Data | Andrew Ng | http://ai.stanford.edu/~ang/papers/cv-final.pdf |
| Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation | http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf |
| CV for detecting and preventing Overfitting | http://www.autonlab.org/tutorials/overfit10.pdf |
| How does CV overcome the Overfitting Problem | http://stats.stackexchange.com/questions/9053/how-does-cross-validation-overcome-the-overfitting-problem |
| Bootstrapping | https://en.wikipedia.org/wiki/Bootstrapping_(statistics) |
| Why Bootstrapping Works? | http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works |
| Good Animation | https://www.stat.auckland.ac.nz/~wild/BootAnim/ |
| Example of Bootstapping | http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm |
| Understanding Bootstapping for Validation and Model Selection | http://stats.stackexchange.com/questions/14516/understanding-bootstrapping-for-validation-and-model-selection?rq=1 |
| Cross Validation vs Bootstrap to estimate prediction error | http://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio |
| Cross-validation vs .632 bootstrapping to evaluate classification performance | http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance |
| https://github.com/Programmer027/Machine-Learning-Tutorials#deep-learning |
| fast.ai - Practical Deep Learning For Coders | http://course.fast.ai/ |
| fast.ai - Cutting Edge Deep Learning For Coders | http://course.fast.ai/part2.html |
| A curated list of awesome Deep Learning tutorials, projects and communities | https://github.com/ChristosChristofidis/awesome-deep-learning |
| Lots of Deep Learning Resources | http://deeplearning4j.org/documentation.html |
| Interesting Deep Learning and NLP Projects (Stanford) | http://cs224d.stanford.edu/reports.html |
| Website | http://cs224d.stanford.edu/ |
| Core Concepts of Deep Learning | https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/ |
| Understanding Natural Language with Deep Neural Networks Using Torch | https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/ |
| Stanford Deep Learning Tutorial | http://ufldl.stanford.edu/tutorial/ |
| Deep Learning FAQs on Quora | https://www.quora.com/topic/Deep-Learning/faq |
| Google+ Deep Learning Page | https://plus.google.com/communities/112866381580457264725 |
| Recent Reddit AMAs related to Deep Learning | http://deeplearning.net/2014/11/22/recent-reddit-amas-about-deep-learning/ |
| Another AMA | https://www.reddit.com/r/IAmA/comments/3mdk9v/we_are_google_researchers_working_on_deep/ |
| Where to Learn Deep Learning? | http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html |
| Deep Learning nvidia concepts | http://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/ |
| Introduction to Deep Learning Using Python (GitHub) | https://github.com/rouseguy/intro2deeplearning |
| Good Introduction Slides | https://speakerdeck.com/bargava/introduction-to-deep-learning |
| Video Lectures Oxford 2015 | https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu |
| Video Lectures Summer School Montreal | http://videolectures.net/deeplearning2015_montreal/ |
| Deep Learning Software List | http://deeplearning.net/software_links/ |
| Hacker's guide to Neural Nets | http://karpathy.github.io/neuralnets/ |
| Top arxiv Deep Learning Papers explained | http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html |
| Geoff Hinton Youtube Vidoes on Deep Learning | https://www.youtube.com/watch?v=IcOMKXAw5VA |
| Awesome Deep Learning Reading List | http://deeplearning.net/reading-list/ |
| Deep Learning Comprehensive Website | http://deeplearning.net/ |
| Software | http://deeplearning.net/software_links/ |
| deeplearning Tutorials | http://deeplearning4j.org/ |
| AWESOME! Deep Learning Tutorial | https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks |
| Deep Learning Basics | http://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html |
| Stanford Tutorials | http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/ |
| Train, Validation & Test in Artificial Neural Networks | http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ |
| Artificial Neural Networks Tutorials | http://stackoverflow.com/questions/478947/what-are-some-good-resources-for-learning-about-artificial-neural-networks |
| Neural Networks FAQs on Stack Overflow | http://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50 |
| Deep Learning Tutorials on deeplearning.net | http://deeplearning.net/tutorial/index.html |
| Neural Networks and Deep Learning Online Book | http://neuralnetworksanddeeplearning.com/ |
| Introduction to Neural Machine Translation with GPUs (part 1) | https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/ |
| Part 2 | https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/ |
| Part 3 | https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/ |
| Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning | https://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/ |
| Torch vs. Theano | http://fastml.com/torch-vs-theano/ |
| dl4j vs. torch7 vs. theano | http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html |
| Deep Learning Libraries by Language | http://www.teglor.com/b/deep-learning-libraries-language-cm569/ |
| Theano | https://en.wikipedia.org/wiki/Theano_(software) |
| Website | http://deeplearning.net/software/theano/ |
| Theano Introduction | http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/ |
| Theano Tutorial | http://outlace.com/Beginner-Tutorial-Theano/ |
| Good Theano Tutorial | http://deeplearning.net/software/theano/tutorial/ |
| Logistic Regression using Theano for classifying digits | http://deeplearning.net/tutorial/logreg.html#logreg |
| MLP using Theano | http://deeplearning.net/tutorial/mlp.html#mlp |
| CNN using Theano | http://deeplearning.net/tutorial/lenet.html#lenet |
| RNNs using Theano | http://deeplearning.net/tutorial/rnnslu.html#rnnslu |
| LSTM for Sentiment Analysis in Theano | http://deeplearning.net/tutorial/lstm.html#lstm |
| RBM using Theano | http://deeplearning.net/tutorial/rbm.html#rbm |
| DBNs using Theano | http://deeplearning.net/tutorial/DBN.html#dbn |
| All Codes | https://github.com/lisa-lab/DeepLearningTutorials |
| Deep Learning Implementation Tutorials - Keras and Lasagne | https://github.com/vict0rsch/deep_learning/ |
| Torch | http://torch.ch/ |
| Torch ML Tutorial | http://code.madbits.com/wiki/doku.php |
| Code | https://github.com/torch/tutorials |
| Intro to Torch | http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf |
| Learning Torch GitHub Repo | https://github.com/chetannaik/learning_torch |
| Awesome-Torch (Repository on GitHub) | https://github.com/carpedm20/awesome-torch |
| Machine Learning using Torch Oxford Univ | https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ |
| Code | https://github.com/oxford-cs-ml-2015 |
| Torch Internals Overview | https://apaszke.github.io/torch-internals.html |
| Torch Cheatsheet | https://github.com/torch/torch7/wiki/Cheatsheet |
| Understanding Natural Language with Deep Neural Networks Using Torch | http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/ |
| Deep Learning for Computer Vision with Caffe and cuDNN | https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/ |
| Website | http://tensorflow.org/ |
| TensorFlow Examples for Beginners | https://github.com/aymericdamien/TensorFlow-Examples |
| Stanford Tensorflow for Deep Learning Research Course | https://web.stanford.edu/class/cs20si/syllabus.html |
| GitHub Repo | https://github.com/chiphuyen/tf-stanford-tutorials |
| Simplified Scikit-learn Style Interface to TensorFlow | https://github.com/tensorflow/skflow |
| Learning TensorFlow GitHub Repo | https://github.com/chetannaik/learning_tensorflow |
| Benchmark TensorFlow GitHub | https://github.com/soumith/convnet-benchmarks/issues/66 |
| Awesome TensorFlow List | https://github.com/jtoy/awesome-tensorflow |
| TensorFlow Book | https://github.com/BinRoot/TensorFlow-Book |
| Android TensorFlow Machine Learning Example | https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc |
| GitHub Repo | https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample |
| Creating Custom Model For Android Using TensorFlow | https://blog.mindorks.com/creating-custom-model-for-android-using-tensorflow-3f963d270bfb |
| GitHub Repo | https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample |
| A Quick Introduction to Neural Networks | https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/ |
| 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 |
| 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 |
| Basic ANN Theory | https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/ |
| Role of Bias in Neural Networks | http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks |
| 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# |
| Backpropagation in Matrix Form | http://sudeepraja.github.io/Neural/ |
| ANN implemented in C++ | AI Junkie | http://www.ai-junkie.com/ann/evolved/nnt6.html |
| Simple Implementation | http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation |
| NN for Beginners | http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of |
| Regression and Classification with NNs (Slides) | http://www.autonlab.org/tutorials/neural13.pdf |
| Another Intro | http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html |
| awesome-rnn: list of resources (GitHub Repo) | https://github.com/kjw0612/awesome-rnn |
| 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/ |
| NLP RNN Representations | http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/ |
| 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 |
| Intro to RNN | http://deeplearning4j.org/recurrentnetwork.html |
| LSTM | http://deeplearning4j.org/lstm.html |
| An application of RNN | http://hackaday.com/2015/10/15/73-computer-scientists-created-a-neural-net-and-you-wont-believe-what-happened-next/ |
| Optimizing RNN Performance | http://svail.github.io/ |
| Simple RNN | http://outlace.com/Simple-Recurrent-Neural-Network/ |
| Auto-Generating Clickbait with RNN | https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/ |
| Sequence Learning using RNN (Slides) | http://www.slideshare.net/indicods/general-sequence-learning-with-recurrent-neural-networks-for-next-ml |
| Machine Translation using RNN (Paper) | http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf |
| Music generation using RNNs (Keras) | https://github.com/MattVitelli/GRUV |
| Using RNN to create on-the-fly dialogue (Keras) | http://neuralniche.com/post/tutorial/ |
| Understanding LSTM Networks | http://colah.github.io/posts/2015-08-Understanding-LSTMs/ |
| LSTM explained | https://apaszke.github.io/lstm-explained.html |
| Beginner’s Guide to LSTM | http://deeplearning4j.org/lstm.html |
| 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 |
| Torch Code for character-level language models using LSTM | https://github.com/karpathy/char-rnn |
| LSTM for Kaggle EEG Detection competition (Torch Code) | https://github.com/apaszke/kaggle-grasp-and-lift |
| LSTM for Sentiment Analysis in Theano | http://deeplearning.net/tutorial/lstm.html#lstm |
| Deep Learning for Visual Q&A | LSTM | CNN | http://avisingh599.github.io/deeplearning/visual-qa/ |
| Code | https://github.com/avisingh599/visual-qa |
| Computer Responds to email using LSTM | Google | http://googleresearch.blogspot.in/2015/11/computer-respond-to-this-email.html |
| 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/ |
| Understanding Natural Language with LSTM Using Torch | http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/ |
| Torch code for Visual Question Answering using a CNN+LSTM model | https://github.com/abhshkdz/neural-vqa |
| LSTM for Human Activity Recognition | https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition/ |
| LSTM vs GRU | http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/ |
| Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models | https://github.com/guillaume-chevalier/seq2seq-signal-prediction |
| Recursive Neural Network (not Recurrent) | https://en.wikipedia.org/wiki/Recursive_neural_network |
| Recursive Neural Tensor Network (RNTN) | http://deeplearning4j.org/recursiveneuraltensornetwork.html |
| word2vec, DBN, RNTN for Sentiment Analysis | http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html |
| Beginner's Guide about RBMs | http://deeplearning4j.org/restrictedboltzmannmachine.html |
| Another Good Tutorial | http://deeplearning.net/tutorial/rbm.html |
| Introduction to RBMs | http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ |
| Hinton's Guide to Training RBMs | https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf |
| RBMs in R | https://github.com/zachmayer/rbm |
| Deep Belief Networks Tutorial | http://deeplearning4j.org/deepbeliefnetwork.html |
| word2vec, DBN, RNTN for Sentiment Analysis | http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html |
| Andrew Ng Sparse Autoencoders pdf | https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf |
| Deep Autoencoders Tutorial | http://deeplearning4j.org/deepautoencoder.html |
| Denoising Autoencoders | http://deeplearning.net/tutorial/dA.html |
| Theano Code | http://deeplearning.net/tutorial/code/dA.py |
| Stacked Denoising Autoencoders | http://deeplearning.net/tutorial/SdA.html#sda |
| An Intuitive Explanation of Convolutional Neural Networks | https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ |
| Awesome Deep Vision: List of Resources (GitHub) | https://github.com/kjw0612/awesome-deep-vision |
| Intro to CNNs | http://deeplearning4j.org/convolutionalnets.html |
| Understanding CNN for NLP | http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/ |
| Stanford Notes | http://vision.stanford.edu/teaching/cs231n/ |
| Codes | http://cs231n.github.io/ |
| GitHub | https://github.com/cs231n/cs231n.github.io |
| JavaScript Library (Browser Based) for CNNs | http://cs.stanford.edu/people/karpathy/convnetjs/ |
| Using CNNs to detect facial keypoints | http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect-facial-keypoints-tutorial/ |
| 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 |
| Interview with Yann LeCun | Kaggle | http://blog.kaggle.com/2014/12/22/convolutional-nets-and-cifar-10-an-interview-with-yan-lecun/ |
| Visualising and Understanding CNNs | https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#natural-language-processing |
| A curated list of speech and natural language processing resources | https://github.com/edobashira/speech-language-processing |
| Understanding Natural Language with Deep Neural Networks Using Torch | http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/ |
| tf-idf explained | http://michaelerasm.us/post/tf-idf-in-10-minutes/ |
| Interesting Deep Learning NLP Projects Stanford | http://cs224d.stanford.edu/reports.html |
| Website | http://cs224d.stanford.edu/ |
| NLP from Scratch | Google Paper | https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/35671.pdf |
| Graph Based Semi Supervised Learning for NLP | http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf |
| Bag of Words | https://en.wikipedia.org/wiki/Bag-of-words_model |
| Classification text with Bag of Words | http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/ |
| Topic Modeling | https://en.wikipedia.org/wiki/Topic_model |
| 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 |
| What is a good explanation of Latent Dirichlet Allocation? | https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation |
| 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 |
| 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/ |
| Difference between LSI and LDA | https://www.quora.com/Whats-the-difference-between-Latent-Semantic-Indexing-LSI-and-Latent-Dirichlet-Allocation-LDA |
| Original LDA Paper | https://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf |
| alpha and beta in LDA | http://datascience.stackexchange.com/questions/199/what-does-the-alpha-and-beta-hyperparameters-contribute-to-in-latent-dirichlet-a |
| Intuitive explanation of the Dirichlet distribution | https://www.quora.com/What-is-an-intuitive-explanation-of-the-Dirichlet-distribution |
| Topic modeling made just simple enough | https://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/ |
| 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 |
| 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 |
| Segmentation of Twitter Timelines via Topic Modeling | http://alexperrier.github.io/jekyll/update/2015/09/16/segmentation_twitter_timelines_lda_vs_lsa.html |
| Topic Modeling of Twitter Followers | http://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html |
| Google word2vec | https://code.google.com/archive/p/word2vec |
| Bag of Words Model Wiki | https://en.wikipedia.org/wiki/Bag-of-words_model |
| word2vec Tutorial | https://rare-technologies.com/word2vec-tutorial/ |
| A closer look at Skip Gram Modeling | http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf |
| 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 |
| 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 |
| Making sense of word2vec | http://rare-technologies.com/making-sense-of-word2vec/ |
| word2vec explained on deeplearning4j | http://deeplearning4j.org/word2vec.html |
| Quora word2vec | https://www.quora.com/How-does-word2vec-work |
| 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 |
| word2vec, DBN, RNTN for Sentiment Analysis | http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html |
| How string clustering works | http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering |
| Levenshtein distance for measuring the difference between two sequences | https://en.wikipedia.org/wiki/Levenshtein_distance |
| Text clustering with Levenshtein distances | http://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances |
| Classification Text with Bag of Words | http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/ |
| Language learning with NLP and reinforcement learning | http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/ |
| 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 |
| What would Shakespeare say (NLP Tutorial) | https://gigadom.wordpress.com/2015/10/02/natural-language-processing-what-would-shakespeare-say/ |
| A closer look at Skip Gram Modeling | http://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#computer-vision |
| Awesome computer vision (github) | https://github.com/jbhuang0604/awesome-computer-vision |
| Awesome deep vision (github) | https://github.com/kjw0612/awesome-deep-vision |
| https://github.com/Programmer027/Machine-Learning-Tutorials#support-vector-machine |
| Highest Voted Questions about SVMs on Cross Validated | http://stats.stackexchange.com/questions/tagged/svm |
| Help me Understand SVMs! | http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines |
| SVM in Layman's terms | https://www.quora.com/What-does-support-vector-machine-SVM-mean-in-laymans-terms |
| How does SVM Work | Comparisons | http://stats.stackexchange.com/questions/23391/how-does-a-support-vector-machine-svm-work |
| A tutorial on SVMs | http://alex.smola.org/papers/2003/SmoSch03b.pdf |
| 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 |
| Introductory Overview of SVMs | http://www.statsoft.com/Textbook/Support-Vector-Machines |
| 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 |
| Trees > SVMs | http://stats.stackexchange.com/questions/57438/why-is-svm-not-so-good-as-decision-tree-on-the-same-data |
| Kernel Logistic Regression vs SVM | http://stats.stackexchange.com/questions/43996/kernel-logistic-regression-vs-svm |
| 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 |
| Optimization Algorithms in Support Vector Machines | http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf |
| Variable Importance from SVM | http://stats.stackexchange.com/questions/2179/variable-importance-from-svm |
| LIBSVM | https://www.csie.ntu.edu.tw/~cjlin/libsvm/ |
| Intro to SVM in R | http://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf |
| What are Kernels in ML and SVM? | https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM |
| Intuition Behind Gaussian Kernel in SVMs? | https://www.quora.com/Support-Vector-Machines/What-is-the-intuition-behind-Gaussian-kernel-in-SVM |
| Platt's Probabilistic Outputs for SVM | http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf |
| Platt Calibration Wiki | https://en.wikipedia.org/wiki/Platt_scaling |
| Why use Platts Scaling | http://stats.stackexchange.com/questions/5196/why-use-platts-scaling |
| Classifier Classification with Platt's Scaling | http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#reinforcement-learning |
| Awesome Reinforcement Learning (GitHub) | https://github.com/aikorea/awesome-rl |
| RL Tutorial Part 1 | http://outlace.com/Reinforcement-Learning-Part-1/ |
| Part 2 | http://outlace.com/Reinforcement-Learning-Part-2/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#decision-trees |
| Wikipedia Page - Lots of Good Info | https://en.wikipedia.org/wiki/Decision_tree_learning |
| FAQs about Decision Trees | http://stats.stackexchange.com/questions/tagged/cart |
| Brief Tour of Trees and Forests | http://statistical-research.com/a-brief-tour-of-the-trees-and-forests/ |
| Tree Based Models in R | http://www.statmethods.net/advstats/cart.html |
| How Decision Trees work? | http://www.aihorizon.com/essays/generalai/decision_trees.htm |
| Weak side of Decision Trees | http://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees |
| Thorough Explanation and different algorithms | http://www.ise.bgu.ac.il/faculty/liorr/hbchap9.pdf |
| What is entropy and information gain in the context of building decision trees? | http://stackoverflow.com/questions/1859554/what-is-entropy-and-information-gain |
| Slides Related to Decision Trees | http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-11-decision-trees |
| 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 |
| 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 |
| Good Article | https://www.mindtools.com/dectree.html |
| Are decision trees almost always binary trees? | http://stats.stackexchange.com/questions/12187/are-decision-trees-almost-always-binary-trees |
| Pruning Decision Trees | https://en.wikipedia.org/wiki/Pruning_(decision_trees) |
| Grafting of Decision Trees | https://en.wikipedia.org/wiki/Grafting_(decision_trees) |
| What is Deviance in context of Decision Trees? | http://stats.stackexchange.com/questions/6581/what-is-deviance-specifically-in-cart-rpart |
| Discover structure behind data with decision trees | http://vooban.com/en/tips-articles-geek-stuff/discover-structure-behind-data-with-decision-trees/ |
| CART vs CTREE | http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees |
| Comparison of complexity or performance | https://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance |
| 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 |
| Good Article on comparison | http://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11 |
| Recursive Partitioning Wikipedia | https://en.wikipedia.org/wiki/Recursive_partitioning |
| CART Explained | http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees |
| 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 |
| Pruning a Tree in R | http://stackoverflow.com/questions/15318409/how-to-prune-a-tree-in-r |
| Does rpart use multivariate splits by default? | http://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default |
| FAQs about Recursive Partitioning | http://stats.stackexchange.com/questions/tagged/rpart |
| party package in R | https://cran.r-project.org/web/packages/party/party.pdf |
| Show volumne in each node using ctree in R | http://stackoverflow.com/questions/13772715/show-volume-in-each-node-using-ctree-plot-in-r |
| How to extract tree structure from ctree function? | http://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function |
| Wikipedia Artice on CHAID | https://en.wikipedia.org/wiki/CHAID |
| Basic Introduction to CHAID | https://smartdrill.com/Introduction-to-CHAID.html |
| Good Tutorial on CHAID | http://www.statsoft.com/Textbook/CHAID-Analysis |
| Wikipedia Article on MARS | https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines |
| Bayesian Learning in Probabilistic Decision Trees | http://www.stats.org.uk/bayesian/Jordan.pdf |
| Probabilistic Trees Research Paper | http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#random-forest--bagging |
| Awesome Random Forest (GitHub)** | https://github.com/kjw0612/awesome-random-forest |
| 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 |
| Measures of variable importance in random forests | http://stats.stackexchange.com/questions/12605/measures-of-variable-importance-in-random-forests |
| Compare R-squared from two different Random Forest models | http://stats.stackexchange.com/questions/13869/compare-r-squared-from-two-different-random-forest-models |
| OOB Estimate Explained | RF vs LDA | https://stat.ethz.ch/education/semesters/ss2012/ams/slides/v10.2.pdf |
| Evaluating Random Forests for Survival Analysis Using Prediction Error Curve | https://www.jstatsoft.org/index.php/jss/article/view/v050i11 |
| 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 |
| 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 |
| FAQs about Random Forest | http://stats.stackexchange.com/questions/tagged/random-forest |
| More FAQs | http://stackoverflow.com/questions/tagged/random-forest |
| Obtaining knowledge from a random forest | http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest |
| 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 |
| https://github.com/Programmer027/Machine-Learning-Tutorials#boosting |
| Boosting for Better Predictions | http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions |
| Boosting Wikipedia Page | https://en.wikipedia.org/wiki/Boosting_(machine_learning) |
| Introduction to Boosted Trees | Tianqi Chen | https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf |
| Gradiet Boosting Wiki | https://en.wikipedia.org/wiki/Gradient_boosting |
| 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 |
| 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 |
| Role of n.minobsinnode parameter of GBM in R | http://stats.stackexchange.com/questions/30645/role-of-n-minobsinnode-parameter-of-gbm-in-r |
| GBM in R | http://www.slideshare.net/mark_landry/gbm-package-in-r |
| FAQs about GBM | http://stats.stackexchange.com/tags/gbm/hot |
| GBM vs xgboost | https://www.kaggle.com/c/higgs-boson/forums/t/9497/r-s-gbm-vs-python-s-xgboost |
| xgboost tuning kaggle | https://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log |
| 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 |
| xgboost survey | https://www.kaggle.com/c/higgs-boson/forums/t/10335/xgboost-post-competition-survey |
| Practical XGBoost in Python online course (free) | http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python |
| AdaBoost Wiki | https://en.wikipedia.org/wiki/AdaBoost |
| Python Code | https://gist.github.com/tristanwietsma/5486024 |
| AdaBoost Sparse Input Support | http://hamzehal.blogspot.com/2014/06/adaboost-sparse-input-support.html |
| adaBag R package | https://cran.r-project.org/web/packages/adabag/adabag.pdf |
| Tutorial | http://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#ensembles |
| Wikipedia Article on Ensemble Learning | https://en.wikipedia.org/wiki/Ensemble_learning |
| Kaggle Ensembling Guide | http://mlwave.com/kaggle-ensembling-guide/ |
| The Power of Simple Ensembles | http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/ |
| Ensemble Learning Intro | http://machine-learning.martinsewell.com/ensembles/ |
| Ensemble Learning Paper | http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf |
| 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/ |
| Ensembling Models with caret | http://stats.stackexchange.com/questions/27361/stacking-ensembling-models-with-caret |
| Bagging vs Boosting vs Stacking | http://stats.stackexchange.com/questions/18891/bagging-boosting-and-stacking-in-machine-learning |
| Good Resources | Kaggle Africa Soil Property Prediction | https://www.kaggle.com/c/afsis-soil-properties/forums/t/10391/best-ensemble-references |
| Boosting vs Bagging | http://www.chioka.in/which-is-better-boosting-or-bagging/ |
| Resources for learning how to implement ensemble methods | http://stats.stackexchange.com/questions/32703/resources-for-learning-how-to-implement-ensemble-methods |
| How are classifications merged in an ensemble classifier? | http://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier |
| https://github.com/Programmer027/Machine-Learning-Tutorials#stacking-models |
| Stacking, Blending and Stacked Generalization | http://www.chioka.in/stacking-blending-and-stacked-generalization/ |
| Stacked Generalization (Stacking) | http://machine-learning.martinsewell.com/ensembles/stacking/ |
| Stacked Generalization: when does it work? | http://www.ijcai.org/Proceedings/97-2/011.pdf |
| Stacked Generalization Paper | http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf |
| https://github.com/Programmer027/Machine-Learning-Tutorials#vapnikchervonenkis-dimension |
| Wikipedia article on VC Dimension | https://en.wikipedia.org/wiki/VC_dimension |
| Intuitive Explanantion of VC Dimension | https://www.quora.com/Explain-VC-dimension-and-shattering-in-lucid-Way |
| Video explaining VC Dimension | https://www.youtube.com/watch?v=puDzy2XmR5c |
| Introduction to VC Dimension | http://www.svms.org/vc-dimension/ |
| FAQs about VC Dimension | http://stats.stackexchange.com/questions/tagged/vc-dimension |
| Do ensemble techniques increase VC-dimension? | http://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension |
| https://github.com/Programmer027/Machine-Learning-Tutorials#bayesian-machine-learning |
| Bayesian Methods for Hackers (using pyMC) | https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers |
| Should all Machine Learning be Bayesian? | http://videolectures.net/bark08_ghahramani_samlbb/ |
| Tutorial on Bayesian Optimisation for Machine Learning | http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf |
| 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 |
| Bayesian Statistics Made Simple | http://greenteapress.com/wp/think-bayes/ |
| Kalman & Bayesian Filters in Python | https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python |
| Markov Chain Wikipedia Page | https://en.wikipedia.org/wiki/Markov_chain |
| https://github.com/Programmer027/Machine-Learning-Tutorials#semi-supervised-learning |
| Wikipedia article on Semi Supervised Learning | https://en.wikipedia.org/wiki/Semi-supervised_learning |
| Tutorial on Semi Supervised Learning | http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf |
| Graph Based Semi Supervised Learning for NLP | http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf |
| Taxonomy | http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_%5B0%5D.pdf |
| Video Tutorial Weka | https://www.youtube.com/watch?v=sWxcIjZFGNM |
| Unsupervised, Supervised and Semi Supervised learning | http://stats.stackexchange.com/questions/517/unsupervised-supervised-and-semi-supervised-learning |
| 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 |
| https://github.com/Programmer027/Machine-Learning-Tutorials#optimization |
| 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 |
| Algorithms for Sparse Optimization and Machine Learning | http://www.ima.umn.edu/2011-2012/W3.26-30.12/activities/Wright-Steve/sjw-ima12 |
| Optimization Algorithms in Machine Learning | http://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf |
| Video Lecture | http://videolectures.net/nips2010_wright_oaml/ |
| Optimization Algorithms for Data Analysis | http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf |
| Video Lectures on Optimization | http://videolectures.net/stephen_j_wright/ |
| Optimization Algorithms in Support Vector Machines | http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf |
| The Interplay of Optimization and Machine Learning Research | http://jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf |
| Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters | http://vooban.com/en/tips-articles-geek-stuff/hyperopt-tutorial-for-optimizing-neural-networks-hyperparameters/ |
| https://github.com/Programmer027/Machine-Learning-Tutorials#other-tutorials |
| this list | https://github.com/ujjwalkarn/DataScienceR |
| this list | https://github.com/ujjwalkarn/DataSciencePython |
| ujjwalkarn.github.io/Machine-Learning-Tutorials | http://ujjwalkarn.github.io/Machine-Learning-Tutorials |
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