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Title: GitHub - hephaex/Machine-Learning-Tutorials: machine learning and deep learning tutorials, articles and other resources · GitHub

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Other Useful Tutorialshttps://github.com/hephaex/Machine-Learning-Tutorials#other
Residual Analysishttps://github.com/hephaex/Machine-Learning-Tutorials#residuals-
Interpreting plot.lm() in Rhttp://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 Plothttp://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions
Outliershttps://github.com/hephaex/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 Nethttps://en.wikipedia.org/wiki/Elastic_net_regularization
Regularization and Variable Selection via the Elastic Nethttps://web.stanford.edu/~hastie/Papers/elasticnet.pdf
Resampling Explainedhttps://en.wikipedia.org/wiki/Resampling_(statistics)
Partioning data set in Rhttp://stackoverflow.com/questions/13536537/partitioning-data-set-in-r-based-on-multiple-classes-of-observations
Implementing hold-out Validaion in Rhttp://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r
2http://www.gettinggeneticsdone.com/2011/02/split-data-frame-into-testing-and.html
Bootstrappinghttps://en.wikipedia.org/wiki/Bootstrapping_(statistics)
Why Bootstrapping Works?http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works
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Cross-validation vs .632 bootstrapping to evaluate classification performancehttp://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance
Introduction to Neural Machine Translation with GPUs (part 1)http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/
Part 2http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-2/
Part 3http://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/
Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learninghttp://devblogs.nvidia.com/parallelforall/deep-speech-accurate-speech-recognition-gpu-accelerated-deep-learning/
How string clustering workshttp://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering
Levenshtein distance for measuring the difference between two sequenceshttps://en.wikipedia.org/wiki/Levenshtein_distance
Text clustering with Levenshtein distanceshttp://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances
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Language learning with NLP and reinforcement learninghttp://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/
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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 Modelinghttp://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf
xgboost tuning kagglehttps://www.kaggle.com/khozzy/rossmann-store-sales/xgboost-parameter-tuning-template/log
xgboost vs gbmhttps://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
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adaBag R packagehttps://cran.r-project.org/web/packages/adabag/adabag.pdf
Tutorialhttp://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf
ujjwalkarn.github.io/Machine-Learning-Tutorialshttp://ujjwalkarn.github.io/Machine-Learning-Tutorials
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