diff --git a/README.md b/README.md
index 07c9532..42e99dc 100644
--- a/README.md
+++ b/README.md
@@ -61,9 +61,9 @@
- [Curated List of Machine Learning Resources](https://hackr.io/tutorials/learn-machine-learning-ml)
-- [In-depth introduction to machine learning in 15 hours of expert videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
+- [In-depth introduction to machine learning in 15 hours of expert videos](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)
-- [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
+- [An Introduction to Statistical Learning](https://www-bcf.usc.edu/~gareth/ISL/)
- [List of Machine Learning University Courses](https://github.com/prakhar1989/awesome-courses#machine-learning)
@@ -77,33 +77,33 @@
- [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/)
+- [The Open Source Data Science Masters](https://datasciencemasters.org/)
-- [Machine Learning FAQs on Cross Validated](http://stats.stackexchange.com/questions/tagged/machine-learning)
+- [Machine Learning FAQs on Cross Validated](https://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)
+- [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables](https://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)
+- [Slides on Several Machine Learning Topics](https://www.slideshare.net/pierluca.lanzi/presentations)
-- [MIT Machine Learning Lecture Slides](http://www.ai.mit.edu/courses/6.867-f04/lectures.html)
+- [MIT Machine Learning Lecture Slides](https://www.ai.mit.edu/courses/6.867-f04/lectures.html)
-- [Comparison Supervised Learning Algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/)
+- [Comparison Supervised Learning Algorithms](https://www.dataschool.io/comparing-supervised-learning-algorithms/)
-- [Learning Data Science Fundamentals](http://www.dataschool.io/learning-data-science-fundamentals/)
+- [Learning Data Science Fundamentals](https://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/)
+- [Statistical Machine Learning Course](https://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)
+- [Twitter's Most Shared #machineLearning Content From The Past 7 Days](https://theherdlocker.com/tweet/popularity/machinelearning)
- [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning)
@@ -129,7 +129,7 @@
- [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)
+- [UC Berkeley CS188 Intro to AI](https://ai.berkeley.edu/home.html), [Lecture Videos](https://ai.berkeley.edu/lecture_videos.html), [2](https://www.youtube.com/watch?v=W1S-HSakPTM)
- [Programming Community Curated Resources for learning Artificial Intelligence](https://hackr.io/tutorials/learn-artificial-intelligence-ai)
@@ -139,7 +139,7 @@
- [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)
+- [TED talks on AI](https://www.ted.com/playlists/310/talks_on_artificial_intelligen)
@@ -147,45 +147,45 @@
- [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)
+- [Simple Implementation of Genetic Algorithms in Python (Part 1)](https://outlace.com/miniga.html), [Part 2](https://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 vs Artificial Neural Networks](https://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 Algorithms Explained in Plain English](https://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)
+ - [Genetic Alogorithms vs Genetic Programming (Quora)](https://www.quora.com/Whats-the-difference-between-Genetic-Algorithms-and-Genetic-Programming), [StackOverflow](https://stackoverflow.com/questions/3819977/what-are-the-differences-between-genetic-algorithms-and-genetic-programming)
## Statistics
-- [Stat Trek Website](http://stattrek.com/) - A dedicated website to teach yourselves Statistics
+- [Stat Trek Website](https://stattrek.com/) - A dedicated website to teach yourselves Statistics
- [Learn Statistics Using Python](https://github.com/rouseguy/intro2stats) - Learn Statistics using an application-centric programming approach
- [Statistics for Hackers | Slides | @jakevdp](https://speakerdeck.com/jakevdp/statistics-for-hackers) - Slides by Jake VanderPlas
-- [Online Statistics Book](http://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics
+- [Online Statistics Book](https://onlinestatbook.com/2/index.html) - An Interactive Multimedia Course for Studying Statistics
-- [What is a Sampling Distribution?](http://stattrek.com/sampling/sampling-distribution.aspx)
+- [What is a Sampling Distribution?](https://stattrek.com/sampling/sampling-distribution.aspx)
- Tutorials
- - [AP Statistics Tutorial](http://stattrek.com/tutorials/ap-statistics-tutorial.aspx)
+ - [AP Statistics Tutorial](https://stattrek.com/tutorials/ap-statistics-tutorial.aspx)
- - [Statistics and Probability Tutorial](http://stattrek.com/tutorials/statistics-tutorial.aspx)
+ - [Statistics and Probability Tutorial](https://stattrek.com/tutorials/statistics-tutorial.aspx)
- - [Matrix Algebra Tutorial](http://stattrek.com/tutorials/matrix-algebra-tutorial.aspx)
+ - [Matrix Algebra Tutorial](https://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)
+- [What are QQ Plots?](https://onlinestatbook.com/2/advanced_graphs/q-q_plots.html)
- [OpenIntro Statistics](https://www.openintro.org/stat/textbook.php?stat_book=os) - Free PDF textbook
@@ -193,41 +193,41 @@
## Useful Blogs
-- [Edwin Chen's Blog](http://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science
+- [Edwin Chen's Blog](https://blog.echen.me/) - A blog about Math, stats, ML, crowdsourcing, data science
-- [The Data School Blog](http://www.dataschool.io/) - Data science for beginners!
+- [The Data School Blog](https://www.dataschool.io/) - Data science for beginners!
-- [ML Wave](http://mlwave.com/) - A blog for Learning Machine Learning
+- [ML Wave](https://mlwave.com/) - A blog for Learning Machine Learning
-- [Andrej Karpathy](http://karpathy.github.io/) - A blog about Deep Learning and Data Science in general
+- [Andrej Karpathy](https://karpathy.github.io/) - A blog about Deep Learning and Data Science in general
-- [Colah's Blog](http://colah.github.io/) - Awesome Neural Networks Blog
+- [Colah's Blog](https://colah.github.io/) - Awesome Neural Networks Blog
-- [Alex Minnaar's Blog](http://alexminnaar.com/) - A blog about Machine Learning and Software Engineering
+- [Alex Minnaar's Blog](https://alexminnaar.com/) - A blog about Machine Learning and Software Engineering
-- [Statistically Significant](http://andland.github.io/) - Andrew Landgraf's Data Science Blog
+- [Statistically Significant](https://andland.github.io/) - Andrew Landgraf's Data Science Blog
-- [Simply Statistics](http://simplystatistics.org/) - A blog by three biostatistics professors
+- [Simply Statistics](https://simplystatistics.org/) - A blog by three biostatistics professors
- [Yanir Seroussi's Blog](https://yanirseroussi.com/) - A blog about Data Science and beyond
-- [fastML](http://fastml.com/) - Machine learning made easy
+- [fastML](https://fastml.com/) - Machine learning made easy
-- [Trevor Stephens Blog](http://trevorstephens.com/) - Trevor Stephens Personal Page
+- [Trevor Stephens Blog](https://trevorstephens.com/) - Trevor Stephens Personal Page
-- [no free hunch | kaggle](http://blog.kaggle.com/) - The Kaggle Blog about all things Data Science
+- [no free hunch | kaggle](https://blog.kaggle.com/) - The Kaggle Blog about all things Data Science
-- [A Quantitative Journey | outlace](http://outlace.com/) - learning quantitative applications
+- [A Quantitative Journey | outlace](https://outlace.com/) - learning quantitative applications
-- [r4stats](http://r4stats.com/) - analyze the world of data science, and to help people learn to use R
+- [r4stats](https://r4stats.com/) - analyze the world of data science, and to help people learn to use R
-- [Variance Explained](http://varianceexplained.org/) - David Robinson's Blog
+- [Variance Explained](https://varianceexplained.org/) - David Robinson's Blog
-- [AI Junkie](http://www.ai-junkie.com/) - a blog about Artificial Intellingence
+- [AI Junkie](https://www.ai-junkie.com/) - a blog about Artificial Intellingence
-- [Deep Learning Blog by Tim Dettmers](http://timdettmers.com/) - Making deep learning accessible
+- [Deep Learning Blog by Tim Dettmers](https://timdettmers.com/) - Making deep learning accessible
-- [J Alammar's Blog](http://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets
+- [J Alammar's Blog](https://jalammar.github.io/)- Blog posts about Machine Learning and Neural Nets
- [Adam Geitgey](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.f7vwrtfne) - Easiest Introduction to machine learning
@@ -259,11 +259,11 @@
- [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/)
+- [Convolution Neural Networks for EEG detection](https://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)
+- [Facebook Recruiting III Explained](https://alexminnaar.com/tag/kaggle-competitions.html)
-- [Predicting CTR with Online ML](http://mlwave.com/predicting-click-through-rates-with-online-machine-learning/)
+- [Predicting CTR with Online ML](https://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/)
@@ -271,8 +271,8 @@
## Cheat Sheets
-- [Probability Cheat Sheet](http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf),
-[Source](http://www.wzchen.com/probability-cheatsheet/)
+- [Probability Cheat Sheet](https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf),
+[Source](https://www.wzchen.com/probability-cheatsheet/)
- [Machine Learning Cheat Sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)
@@ -282,19 +282,19 @@
## Classification
-- [Does Balancing Classes Improve Classifier Performance?](http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/)
+- [Does Balancing Classes Improve Classifier Performance?](https://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)
+- [What is Deviance?](https://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)
+- [When to choose which machine learning classifier?](https://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))
+- [ROC and AUC Explained](https://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/)
+- [Simple guide to confusion matrix terminology](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)
@@ -303,15 +303,15 @@
- [General](#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)
+ - [Assumptions of Linear Regression](https://pareonline.net/getvn.asp?n=2&v=8), [Stack Exchange](https://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)
+ - [Linear Regression Comprehensive Resource](https://people.duke.edu/~rnau/regintro.htm)
- - [Applying and Interpreting Linear Regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/)
+ - [Applying and Interpreting Linear Regression](https://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)
+ - [What does having constant variance in a linear regression model mean?](https://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)
+ - [Difference between linear regression on y with x and x with y](https://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)
- Multicollinearity and VIF
@@ -322,15 +322,15 @@
- [Residual Analysis](#residuals-)
- - [Interpreting plot.lm() in R](http://stats.stackexchange.com/questions/58141/interpreting-plot-lm)
+ - [Interpreting plot.lm() in R](https://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)
+ - [How to interpret a QQ plot?](https://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)
+ - [Interpreting Residuals vs Fitted Plot](https://stats.stackexchange.com/questions/76226/interpreting-the-residuals-vs-fitted-values-plot-for-verifying-the-assumptions)
- [Outliers](#outliers-)
- - [How should outliers be dealt with?](http://stats.stackexchange.com/questions/175/how-should-outliers-be-dealt-with-in-linear-regression-analysis)
+ - [How should outliers be dealt with?](https://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
@@ -342,17 +342,17 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression)
-- [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html)
+- [Geometric Intuition of Logistic Regression](https://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)
+- [Obtaining predicted categories (choosing threshold)](https://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)
+- [Residuals in logistic regression](https://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)
+- [Difference between logit and probit models](https://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)
+- [Pseudo R2 for Logistic Regression](https://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](https://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](https://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/)
+- [Guide to an in-depth understanding of logistic regression](https://www.dataschool.io/guide-to-logistic-regression/)
@@ -360,123 +360,123 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+- [Partioning data set in R](https://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)
+- [Implementing hold-out Validaion in R](https://stackoverflow.com/questions/22972854/how-to-implement-a-hold-out-validation-in-r), [2](https://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)
+ - [How to use cross-validation in predictive modeling](https://stuartlacy.co.uk/2016/02/04/how-to-correctly-use-cross-validation-in-predictive-modelling/)
+ - [Training with Full dataset after CV?](https://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)
+ - [Which CV method is best?](https://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)
+ - [Variance Estimates in k-fold CV](https://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)
+ - [Is CV a subsitute for Validation Set?](https://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)
+ - [Choice of k in k-fold CV](https://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)
+ - [CV for ensemble learning](https://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)
+ - [k-fold CV in R](https://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret)
- - [Good Resources](http://www.chioka.in/tag/cross-validation/)
+ - [Good Resources](https://www.chioka.in/tag/cross-validation/)
- Overfitting and Cross Validation
- - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf)
+ - [Preventing Overfitting the Cross Validation Data | Andrew Ng](https://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)
+ - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](https://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf)
- - [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf)
+ - [CV for detecting and preventing Overfitting](https://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)
+ - [How does CV overcome the Overfitting Problem](https://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)
+ - [Why Bootstrapping Works?](https://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)
+ - [Example of Bootstapping](https://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)
+ - [Understanding Bootstapping for Validation and Model Selection](https://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)
+ - [Cross Validation vs Bootstrap to estimate prediction error](https://stats.stackexchange.com/questions/18348/differences-between-cross-validation-and-bootstrapping-to-estimate-the-predictio), [Cross-validation vs .632 bootstrapping to evaluate classification performance](https://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)
## Deep Learning
-- [fast.ai - Practical Deep Learning For Coders](http://course.fast.ai/)
+- [fast.ai - Practical Deep Learning For Coders](https://course.fast.ai/)
-- [fast.ai - Cutting Edge Deep Learning For Coders](http://course.fast.ai/part2.html)
+- [fast.ai - Cutting Edge Deep Learning For Coders](https://course.fast.ai/part2.html)
- [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/ChristosChristofidis/awesome-deep-learning)
- **[Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)**
-- [Lots of Deep Learning Resources](http://deeplearning4j.org/documentation.html)
+- [Lots of Deep Learning Resources](https://deeplearning4j.org/documentation.html)
-- [Interesting Deep Learning and NLP Projects (Stanford)](http://cs224d.stanford.edu/reports.html), [Website](http://cs224d.stanford.edu/)
+- [Interesting Deep Learning and NLP Projects (Stanford)](https://cs224d.stanford.edu/reports.html), [Website](https://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/)
+- [Stanford Deep Learning Tutorial](https://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/)
+- [Recent Reddit AMAs related to Deep Learning](https://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)
+- [Where to Learn Deep Learning?](https://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/)
+- [Deep Learning nvidia concepts](https://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/)
+- [Video Lectures Oxford 2015](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu), [Video Lectures Summer School Montreal](https://videolectures.net/deeplearning2015_montreal/)
-- [Deep Learning Software List](http://deeplearning.net/software_links/)
+- [Deep Learning Software List](https://deeplearning.net/software_links/)
-- [Hacker's guide to Neural Nets](http://karpathy.github.io/neuralnets/)
+- [Hacker's guide to Neural Nets](https://karpathy.github.io/neuralnets/)
-- [Top arxiv Deep Learning Papers explained](http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html)
+- [Top arxiv Deep Learning Papers explained](https://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/)
+- [Awesome Deep Learning Reading List](https://deeplearning.net/reading-list/)
-- [Deep Learning Comprehensive Website](http://deeplearning.net/), [Software](http://deeplearning.net/software_links/)
+- [Deep Learning Comprehensive Website](https://deeplearning.net/), [Software](https://deeplearning.net/software_links/)
-- [deeplearning Tutorials](http://deeplearning4j.org/)
+- [deeplearning Tutorials](https://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)
+- [Deep Learning Basics](https://alexminnaar.com/deep-learning-basics-neural-networks-backpropagation-and-stochastic-gradient-descent.html)
- [Intuition Behind Backpropagation](https://medium.com/spidernitt/breaking-down-neural-networks-an-intuitive-approach-to-backpropagation-3b2ff958794c)
-- [Stanford Tutorials](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/)
+- [Stanford Tutorials](https://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)
+- [Train, Validation & Test in Artificial Neural Networks](https://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)
+- [Artificial Neural Networks Tutorials](https://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)
+- [Neural Networks FAQs on Stack Overflow](https://stackoverflow.com/questions/tagged/neural-network?sort=votes&pageSize=50)
-- [Deep Learning Tutorials on deeplearning.net](http://deeplearning.net/tutorial/index.html)
+- [Deep Learning Tutorials on deeplearning.net](https://deeplearning.net/tutorial/index.html)
-- [Neural Networks and Deep Learning Online Book](http://neuralnetworksanddeeplearning.com/)
+- [Neural Networks and Deep Learning Online Book](https://neuralnetworksanddeeplearning.com/)
- Neural Machine Translation
@@ -490,46 +490,46 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- Deep Learning Frameworks
- - [Torch vs. Theano](http://fastml.com/torch-vs-theano/)
+ - [Torch vs. Theano](https://fastml.com/torch-vs-theano/)
- - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html)
+ - [dl4j vs. torch7 vs. theano](https://deeplearning4j.org/compare-dl4j-torch7-pylearn.html)
- - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/)
+ - [Deep Learning Libraries by Language](https://www.teglor.com/b/deep-learning-libraries-language-cm569/)
- [Theano](https://en.wikipedia.org/wiki/Theano_(software))
- - [Website](http://deeplearning.net/software/theano/)
+ - [Website](https://deeplearning.net/software/theano/)
- - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/)
+ - [Theano Introduction](https://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/)
- - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/)
+ - [Theano Tutorial](https://outlace.com/Beginner-Tutorial-Theano/)
- - [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/)
+ - [Good Theano Tutorial](https://deeplearning.net/software/theano/tutorial/)
- - [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg)
+ - [Logistic Regression using Theano for classifying digits](https://deeplearning.net/tutorial/logreg.html#logreg)
- - [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp)
+ - [MLP using Theano](https://deeplearning.net/tutorial/mlp.html#mlp)
- - [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet)
+ - [CNN using Theano](https://deeplearning.net/tutorial/lenet.html#lenet)
- - [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu)
+ - [RNNs using Theano](https://deeplearning.net/tutorial/rnnslu.html#rnnslu)
- - [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm)
+ - [LSTM for Sentiment Analysis in Theano](https://deeplearning.net/tutorial/lstm.html#lstm)
- - [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm)
+ - [RBM using Theano](https://deeplearning.net/tutorial/rbm.html#rbm)
- - [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn)
+ - [DBNs using Theano](https://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](https://torch.ch/)
- - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials)
+ - [Torch ML Tutorial](https://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)
+ - [Intro to Torch](https://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf)
- [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch)
@@ -541,13 +541,13 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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/)
+ - [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/)
- Caffe
- [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/)
- TensorFlow
- - [Website](http://tensorflow.org/)
+ - [Website](https://tensorflow.org/)
- [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples)
@@ -577,80 +577,80 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+ - [Implementing a Neural Network from scratch](https://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)
+ - [Speeding up your Neural Network with Theano and the gpu](https://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)
+ - [Role of Bias in Neural Networks](https://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#)
+ - [Choosing number of hidden layers and nodes](https://stackoverflow.com/questions/3345079/estimating-the-number-of-neurons-and-number-of-layers-of-an-artificial-neural-ne),[2](https://stackoverflow.com/questions/10565868/multi-layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](https://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/)
+ - [Backpropagation in Matrix Form](https://sudeepraja.github.io/Neural/)
- - [ANN implemented in C++ | AI Junkie](http://www.ai-junkie.com/ann/evolved/nnt6.html)
+ - [ANN implemented in C++ | AI Junkie](https://www.ai-junkie.com/ann/evolved/nnt6.html)
- - [Simple Implementation](http://stackoverflow.com/questions/15395835/simple-multi-layer-neural-network-implementation)
+ - [Simple Implementation](https://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)
+ - [NN for Beginners](https://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)
+ - [Regression and Classification with NNs (Slides)](https://www.autonlab.org/tutorials/neural13.pdf)
- - [Another Intro](http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html)
+ - [Another Intro](https://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html)
- Recurrent and LSTM Networks
- [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/)
+ - [Recurrent Neural Net Tutorial Part 1](https://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/), [Part 2](https://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](https://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/)
+ - [NLP RNN Representations](https://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)
+ - [The Unreasonable effectiveness of RNNs](https://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)
+ - [Intro to RNN](https://deeplearning4j.org/recurrentnetwork.html), [LSTM](https://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/)
+ - [An application of RNN](https://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/)
+ - [Optimizing RNN Performance](https://svail.github.io/)
- - [Simple RNN](http://outlace.com/Simple-Recurrent-Neural-Network/)
+ - [Simple RNN](https://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)
+ - [Sequence Learning using RNN (Slides)](https://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)
+ - [Machine Translation using RNN (Paper)](https://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/)
+ - [Using RNN to create on-the-fly dialogue (Keras)](https://neuralniche.com/post/tutorial/)
- Long Short Term Memory (LSTM)
- - [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
+ - [Understanding LSTM Networks](https://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)
+ - [Beginner’s Guide to LSTM](https://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)
+ - [Implementing LSTM from scratch](https://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)
+ - [LSTM for Sentiment Analysis in Theano](https://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)
+ - [Deep Learning for Visual Q&A | LSTM | CNN](https://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)
+ - [Computer Responds to email using LSTM | Google](https://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/)
+ - [LSTM dramatically improves Google Voice Search](https://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html), [Another Article](https://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/)
+ - [Understanding Natural Language with LSTM Using Torch](https://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)
@@ -658,7 +658,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- Gated Recurrent Units (GRU)
- - [LSTM vs GRU](http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/)
+ - [LSTM vs GRU](https://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)
@@ -667,27 +667,27 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Recursive Neural Network (not Recurrent)](https://en.wikipedia.org/wiki/Recursive_neural_network)
- - [Recursive Neural Tensor Network (RNTN)](http://deeplearning4j.org/recursiveneuraltensornetwork.html)
+ - [Recursive Neural Tensor Network (RNTN)](https://deeplearning4j.org/recursiveneuraltensornetwork.html)
- - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)
+ - [word2vec, DBN, RNTN for Sentiment Analysis ](https://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)
- Restricted Boltzmann Machine
- - [Beginner's Guide about RBMs](http://deeplearning4j.org/restrictedboltzmannmachine.html)
+ - [Beginner's Guide about RBMs](https://deeplearning4j.org/restrictedboltzmannmachine.html)
- - [Another Good Tutorial](http://deeplearning.net/tutorial/rbm.html)
+ - [Another Good Tutorial](https://deeplearning.net/tutorial/rbm.html)
- - [Introduction to RBMs](http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/)
+ - [Introduction to RBMs](https://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)
+ - [Deep Belief Networks Tutorial](https://deeplearning4j.org/deepbeliefnetwork.html)
- - [word2vec, DBN, RNTN for Sentiment Analysis ](http://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)
+ - [word2vec, DBN, RNTN for Sentiment Analysis ](https://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)
@@ -695,11 +695,11 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Andrew Ng Sparse Autoencoders pdf](https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf)
- - [Deep Autoencoders Tutorial](http://deeplearning4j.org/deepautoencoder.html)
+ - [Deep Autoencoders Tutorial](https://deeplearning4j.org/deepautoencoder.html)
- - [Denoising Autoencoders](http://deeplearning.net/tutorial/dA.html), [Theano Code](http://deeplearning.net/tutorial/code/dA.py)
+ - [Denoising Autoencoders](https://deeplearning.net/tutorial/dA.html), [Theano Code](https://deeplearning.net/tutorial/code/dA.py)
- - [Stacked Denoising Autoencoders](http://deeplearning.net/tutorial/SdA.html#sda)
+ - [Stacked Denoising Autoencoders](https://deeplearning.net/tutorial/SdA.html#sda)
@@ -710,19 +710,19 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Awesome Deep Vision: List of Resources (GitHub)](https://github.com/kjw0612/awesome-deep-vision)
- - [Intro to CNNs](http://deeplearning4j.org/convolutionalnets.html)
+ - [Intro to CNNs](https://deeplearning4j.org/convolutionalnets.html)
- - [Understanding CNN for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)
+ - [Understanding CNN for NLP](https://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)
+ - [Stanford Notes](https://vision.stanford.edu/teaching/cs231n/), [Codes](https://cs231n.github.io/), [GitHub](https://github.com/cs231n/cs231n.github.io)
- - [JavaScript Library (Browser Based) for CNNs](http://cs.stanford.edu/people/karpathy/convnetjs/)
+ - [JavaScript Library (Browser Based) for CNNs](https://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/)
+ - [Using CNNs to detect facial keypoints](https://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)
+ - [Deep learning to classify business photos at Yelp](https://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/)
+ - [Interview with Yann LeCun | Kaggle](https://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)
@@ -746,35 +746,35 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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/)
+- [Understanding Natural Language with Deep Neural Networks Using Torch](https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/)
-- [tf-idf explained](http://michaelerasm.us/post/tf-idf-in-10-minutes/)
+- [tf-idf explained](https://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/)
+- [Interesting Deep Learning NLP Projects Stanford](https://cs224d.stanford.edu/reports.html), [Website](https://cs224d.stanford.edu/)
- [The Stanford NLP Group](https://nlp.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)
+- [Graph Based Semi Supervised Learning for NLP](https://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/)
+ - [Classification text with Bag of Words](https://fastml.com/classifying-text-with-bag-of-words-a-tutorial/)
- Topic Modeling
- [Topic Modeling Wikipedia](https://en.wikipedia.org/wiki/Topic_model)
- - [**Probabilistic Topic Models Princeton PDF**](http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf)
+ - [**Probabilistic Topic Models Princeton PDF**](https://www.cs.columbia.edu/~blei/papers/Blei2012.pdf)
- [LDA Wikipedia](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation), [LSA Wikipedia](https://en.wikipedia.org/wiki/Latent_semantic_analysis), [Probabilistic LSA Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis)
- [What is a good explanation of Latent Dirichlet Allocation (LDA)?](https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation)
- - [**Introduction to LDA**](http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](http://confusedlanguagetech.blogspot.in/2012/07/jordan-boyd-graber-and-philip-resnik.html)
+ - [**Introduction to LDA**](https://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/), [Another good explanation](https://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/)
+ - [The LDA Buffet - Intuitive Explanation](https://www.matthewjockers.net/2011/09/29/the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/)
- [Your Guide to Latent Dirichlet Allocation (LDA)](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d)
@@ -782,25 +782,25 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+ - [alpha and beta in LDA](https://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)
- [topicmodels: An R Package for Fitting Topic Models](https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf)
- [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)
+ - [Online LDA](https://alexminnaar.com/online-latent-dirichlet-allocation-the-best-option-for-topic-modeling-with-large-data-sets.html), [Online LDA with Spark](https://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)
+ - [LDA in Scala](https://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-i-the-theory.html), [Part 2](https://alexminnaar.com/latent-dirichlet-allocation-in-scala-part-ii-the-code.html)
- [Segmentation of Twitter Timelines via Topic Modeling](https://alexisperrier.com/nlp/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)
+ - [Topic Modeling of Twitter Followers](https://alexperrier.github.io/jekyll/update/2015/09/04/topic-modeling-of-twitter-followers.html)
- [Multilingual Latent Dirichlet Allocation (LDA)](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA). ([Tutorial here](https://github.com/ArtificiAI/Multilingual-Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb))
- [Deep Belief Nets for Topic Modeling](https://github.com/larsmaaloee/deep-belief-nets-for-topic-modeling)
- - [Gaussian LDA for Topic Models with Word Embeddings](http://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf)
+ - [Gaussian LDA for Topic Models with Word Embeddings](https://www.cs.cmu.edu/~rajarshd/papers/acl2015.pdf)
- Python
- [Series of lecture notes for probabilistic topic models written in ipython notebook](https://github.com/arongdari/topic-model-lecture-note)
- [Implementation of various topic models in Python](https://github.com/arongdari/python-topic-model)
@@ -815,33 +815,33 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+ - [A closer look at Skip Gram Modeling](https://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)
+ - [Skip Gram Model Tutorial](https://alexminnaar.com/word2vec-tutorial-part-i-the-skip-gram-model.html), [CBoW Model](https://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/)
+ - [Making sense of word2vec](https://rare-technologies.com/making-sense-of-word2vec/)
- - [word2vec explained on deeplearning4j](http://deeplearning4j.org/word2vec.html)
+ - [word2vec explained on deeplearning4j](https://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)
+ - [word2vec, DBN, RNTN for Sentiment Analysis ](https://deeplearning4j.org/zh-sentiment_analysis_word2vec.html)
- Text Clustering
- - [How string clustering works](http://stackoverflow.com/questions/8196371/how-clustering-works-especially-string-clustering)
+ - [How string clustering works](https://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)
+ - [Text clustering with Levenshtein distances](https://stackoverflow.com/questions/21511801/text-clustering-with-levenshtein-distances)
- Text Classification
- - [Classification Text with Bag of Words](http://fastml.com/classifying-text-with-bag-of-words-a-tutorial/)
+ - [Classification Text with Bag of Words](https://fastml.com/classifying-text-with-bag-of-words-a-tutorial/)
- Named Entity Recognitation
@@ -849,13 +849,13 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Named Entity Recognition: Applications and Use Cases- Towards Data Science](https://towardsdatascience.com/named-entity-recognition-applications-and-use-cases-acdbf57d595e)
-- [Language learning with NLP and reinforcement learning](http://blog.dennybritz.com/2015/09/11/reimagining-language-learning-with-nlp-and-reinforcement-learning/)
+- [Language learning with NLP and reinforcement learning](https://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)
+- [A closer look at Skip Gram Modeling](https://homepages.inf.ed.ac.uk/ballison/pdf/lrec_skipgrams.pdf)
@@ -869,39 +869,39 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
## Support Vector Machine
-- [Highest Voted Questions about SVMs on Cross Validated](http://stats.stackexchange.com/questions/tagged/svm)
+- [Highest Voted Questions about SVMs on Cross Validated](https://stats.stackexchange.com/questions/tagged/svm)
-- [Help me Understand SVMs!](http://stats.stackexchange.com/questions/3947/help-me-understand-support-vector-machines)
+- [Help me Understand SVMs!](https://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)
+- [How does SVM Work | Comparisons](https://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)
+- [A tutorial on SVMs](https://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)
+- [Practical Guide to SVC](https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf), [Slides](https://www.csie.ntu.edu.tw/~cjlin/talks/freiburg.pdf)
-- [Introductory Overview of SVMs](http://www.statsoft.com/Textbook/Support-Vector-Machines)
+- [Introductory Overview of SVMs](https://www.statsoft.com/Textbook/Support-Vector-Machines)
- Comparisons
- - [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)
+ - [SVMs > ANNs](https://stackoverflow.com/questions/6699222/support-vector-machines-better-than-artificial-neural-networks-in-which-learn?rq=1), [ANNs > SVMs](https://stackoverflow.com/questions/11632516/what-are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](https://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)
+ - [Trees > SVMs](https://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)
+ - [Kernel Logistic Regression vs SVM](https://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)
+ - [Logistic Regression vs SVM](https://stats.stackexchange.com/questions/58684/regularized-logistic-regression-and-support-vector-machine), [2](https://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)
+- [Optimization Algorithms in Support Vector Machines](https://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf)
-- [Variable Importance from SVM](http://stats.stackexchange.com/questions/2179/variable-importance-from-svm)
+- [Variable Importance from SVM](https://stats.stackexchange.com/questions/2179/variable-importance-from-svm)
- Software
- [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)
+ - [Intro to SVM in R](https://cbio.ensmp.fr/~jvert/svn/tutorials/practical/svmbasic/svmbasic_notes.pdf)
- Kernels
- [What are Kernels in ML and SVM?](https://www.quora.com/What-are-Kernels-in-Machine-Learning-and-SVM)
@@ -910,13 +910,13 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- Probabilities post SVM
- - [Platt's Probabilistic Outputs for SVM](http://www.csie.ntu.edu.tw/~htlin/paper/doc/plattprob.pdf)
+ - [Platt's Probabilistic Outputs for SVM](https://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)
+ - [Why use Platts Scaling](https://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/)
+ - [Classifier Classification with Platt's Scaling](https://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/)
@@ -925,7 +925,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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/)
+- [RL Tutorial Part 1](https://outlace.com/Reinforcement-Learning-Part-1/), [Part 2](https://outlace.com/Reinforcement-Learning-Part-2/)
@@ -933,67 +933,67 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+- [FAQs about Decision Trees](https://stats.stackexchange.com/questions/tagged/cart)
- [Brief Tour of Trees and Forests](https://statistical-research.com/index.php/2013/04/29/a-brief-tour-of-the-trees-and-forests/)
-- [Tree Based Models in R](http://www.statmethods.net/advstats/cart.html)
+- [Tree Based Models in R](https://www.statmethods.net/advstats/cart.html)
-- [How Decision Trees work?](http://www.aihorizon.com/essays/generalai/decision_trees.htm)
+- [How Decision Trees work?](https://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)
+- [Weak side of Decision Trees](https://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)
+- [Thorough Explanation and different algorithms](https://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)
+- [What is entropy and information gain in the context of building decision trees?](https://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)
+- [Slides Related to Decision Trees](https://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)
+- [How do decision tree learning algorithms deal with missing values?](https://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)
+- [Are decision trees almost always binary trees?](https://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)
+- [What is Deviance in context of Decision Trees?](https://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/) - Grow and plot a decision tree to automatically figure out hidden rules in your data
+- [Discover structure behind data with decision trees](https://vooban.com/en/tips-articles-geek-stuff/discover-structure-behind-data-with-decision-trees/) - Grow and plot a decision tree to automatically figure out hidden rules in your data
- Comparison of Different Algorithms
- - [CART vs CTREE](http://stats.stackexchange.com/questions/12140/conditional-inference-trees-vs-traditional-decision-trees)
+ - [CART vs CTREE](https://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)
+ - [CHAID vs CART](https://stats.stackexchange.com/questions/61230/chaid-vs-crt-or-cart) , [CART vs CHAID](https://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)
+ - [Good Article on comparison](https://www.ftpress.com/articles/article.aspx?p=2248639&seqNum=11)
- CART
- [Recursive Partitioning Wikipedia](https://en.wikipedia.org/wiki/Recursive_partitioning)
- - [CART Explained](http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees)
+ - [CART Explained](https://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)
+ - [How to measure/rank “variable importance” when using CART?](https://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)
+ - [Pruning a Tree in R](https://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)
+ - [Does rpart use multivariate splits by default?](https://stats.stackexchange.com/questions/4356/does-rpart-use-multivariate-splits-by-default)
- - [FAQs about Recursive Partitioning](http://stats.stackexchange.com/questions/tagged/rpart)
+ - [FAQs about Recursive Partitioning](https://stats.stackexchange.com/questions/tagged/rpart)
- CTREE
- [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)
+ - [Show volumne in each node using ctree in R](https://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)
+ - [How to extract tree structure from ctree function?](https://stackoverflow.com/questions/8675664/how-to-extract-tree-structure-from-ctree-function)
- CHAID
@@ -1001,7 +1001,7 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Basic Introduction to CHAID](https://smartdrill.com/Introduction-to-CHAID.html)
- - [Good Tutorial on CHAID](http://www.statsoft.com/Textbook/CHAID-Analysis)
+ - [Good Tutorial on CHAID](https://www.statsoft.com/Textbook/CHAID-Analysis)
- MARS
@@ -1009,9 +1009,9 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- Probabilistic Decision Trees
- - [Bayesian Learning in Probabilistic Decision Trees](http://www.stats.org.uk/bayesian/Jordan.pdf)
+ - [Bayesian Learning in Probabilistic Decision Trees](https://www.stats.org.uk/bayesian/Jordan.pdf)
- - [Probabilistic Trees Research Paper](http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf)
+ - [Probabilistic Trees Research Paper](https://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf)
@@ -1021,29 +1021,29 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+- [Measures of variable importance in random forests](https://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)
+- [Compare R-squared from two different Random Forest models](https://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)
+- [Why doesn't Random Forest handle missing values in predictors?](https://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)
+- [How to build random forests in R with missing (NA) values?](https://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)
+- [FAQs about Random Forest](https://stats.stackexchange.com/questions/tagged/random-forest), [More FAQs](https://stackoverflow.com/questions/tagged/random-forest)
-- [Obtaining knowledge from a random forest](http://stats.stackexchange.com/questions/21152/obtaining-knowledge-from-a-random-forest)
+- [Obtaining knowledge from a random forest](https://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)
+- [Some Questions for R implementation](https://stackoverflow.com/questions/20537186/getting-predictions-after-rfimpute), [2](https://stats.stackexchange.com/questions/81609/whether-preprocessing-is-needed-before-prediction-using-finalmodel-of-randomfore), [3](https://stackoverflow.com/questions/17059432/random-forest-package-in-r-shows-error-during-prediction-if-there-are-new-fact)
## Boosting
-- [Boosting for Better Predictions](http://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions)
+- [Boosting for Better Predictions](https://www.datasciencecentral.com/profiles/blogs/boosting-algorithms-for-better-predictions)
- [Boosting Wikipedia Page](https://en.wikipedia.org/wiki/Boosting_(machine_learning))
@@ -1053,15 +1053,15 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.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)
+ - [Guidelines for GBM parameters in R](https://stats.stackexchange.com/questions/25748/what-are-some-useful-guidelines-for-gbm-parameters), [Strategy to set parameters](https://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)
+ - [Meaning of Interaction Depth](https://stats.stackexchange.com/questions/16501/what-does-interaction-depth-mean-in-gbm), [2](https://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)
+ - [Role of n.minobsinnode parameter of GBM in R](https://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)
+ - [GBM in R](https://www.slideshare.net/mark_landry/gbm-package-in-r)
- - [FAQs about GBM](http://stats.stackexchange.com/tags/gbm/hot)
+ - [FAQs about GBM](https://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)
@@ -1073,17 +1073,17 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+ - [Practical XGBoost in Python online course (free)](https://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python)
- AdaBoost
- [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)
+ - [AdaBoost Sparse Input Support](https://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)
+ - [Tutorial](https://math.mit.edu/~rothvoss/18.304.3PM/Presentations/1-Eric-Boosting304FinalRpdf.pdf)
- CatBoost
@@ -1103,39 +1103,39 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Wikipedia Article on Ensemble Learning](https://en.wikipedia.org/wiki/Ensemble_learning)
-- [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/)
+- [Kaggle Ensembling Guide](https://mlwave.com/kaggle-ensembling-guide/)
-- [The Power of Simple Ensembles](http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/)
+- [The Power of Simple Ensembles](https://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/)
-- [Ensemble Learning Intro](http://machine-learning.martinsewell.com/ensembles/)
+- [Ensemble Learning Intro](https://machine-learning.martinsewell.com/ensembles/)
-- [Ensemble Learning Paper](http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/springerEBR09.pdf)
+- [Ensemble Learning Paper](https://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 R](https://amunategui.github.io/blending-models/), [Ensembling Regression Models in R](https://stats.stackexchange.com/questions/26790/ensembling-regression-models), [Intro to Ensembles in R](https://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)
+- [Ensembling Models with caret](https://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)
+- [Bagging vs Boosting vs Stacking](https://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/)
+- [Boosting vs Bagging](https://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)
+- [Resources for learning how to implement ensemble methods](https://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)
+- [How are classifications merged in an ensemble classifier?](https://stats.stackexchange.com/questions/21502/how-are-classifications-merged-in-an-ensemble-classifier)
## Stacking Models
-- [Stacking, Blending and Stacked Generalization](http://www.chioka.in/stacking-blending-and-stacked-generalization/)
+- [Stacking, Blending and Stacked Generalization](https://www.chioka.in/stacking-blending-and-stacked-generalization/)
-- [Stacked Generalization (Stacking)](http://machine-learning.martinsewell.com/ensembles/stacking/)
+- [Stacked Generalization (Stacking)](https://machine-learning.martinsewell.com/ensembles/stacking/)
-- [Stacked Generalization: when does it work?](http://www.ijcai.org/Proceedings/97-2/011.pdf)
+- [Stacked Generalization: when does it work?](https://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)
+- [Stacked Generalization Paper](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.1533&rep=rep1&type=pdf)
@@ -1147,11 +1147,11 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [Video explaining VC Dimension](https://www.youtube.com/watch?v=puDzy2XmR5c)
-- [Introduction to VC Dimension](http://www.svms.org/vc-dimension/)
+- [Introduction to VC Dimension](https://www.svms.org/vc-dimension/)
-- [FAQs about VC Dimension](http://stats.stackexchange.com/questions/tagged/vc-dimension)
+- [FAQs about VC Dimension](https://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)
+- [Do ensemble techniques increase VC-dimension?](https://stats.stackexchange.com/questions/78076/do-ensemble-techniques-increase-vc-dimension)
@@ -1160,13 +1160,13 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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/)
+- [Should all Machine Learning be Bayesian?](https://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)
+- [Tutorial on Bayesian Optimisation for Machine Learning](https://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 Reasoning and Deep Learning](https://blog.shakirm.com/2015/10/bayesian-reasoning-and-deep-learning/), [Slides](https://blog.shakirm.com/wp-content/uploads/2015/10/Bayes_Deep.pdf)
-- [Bayesian Statistics Made Simple](http://greenteapress.com/wp/think-bayes/)
+- [Bayesian Statistics Made Simple](https://greenteapress.com/wp/think-bayes/)
- [Kalman & Bayesian Filters in Python](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python)
@@ -1179,38 +1179,38 @@ Elastic Net](https://web.stanford.edu/~hastie/Papers/elasticnet.pdf)
- [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)
+- [Tutorial on Semi Supervised Learning](https://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)
+- [Graph Based Semi Supervised Learning for NLP](https://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_[0].pdf)
+- [Taxonomy](https://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/taxo_[0].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)
+- [Unsupervised, Supervised and Semi Supervised learning](https://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)
+- [Research Papers 1](https://mlg.eng.cam.ac.uk/zoubin/papers/zglactive.pdf), [2](https://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf), [3](https://icml.cc/2012/papers/616.pdf)
## 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)
+- [Mean Variance Portfolio Optimization with R and Quadratic Programming](https://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)
+- [Algorithms for Sparse Optimization and Machine Learning](https://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 in Machine Learning](https://pages.cs.wisc.edu/~swright/nips2010/sjw-nips10.pdf), [Video Lecture](https://videolectures.net/nips2010_wright_oaml/)
-- [Optimization Algorithms for Data Analysis](http://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf)
+- [Optimization Algorithms for Data Analysis](https://www.birs.ca/workshops/2011/11w2035/files/Wright.pdf)
-- [Video Lectures on Optimization](http://videolectures.net/stephen_j_wright/)
+- [Video Lectures on Optimization](https://videolectures.net/stephen_j_wright/)
-- [Optimization Algorithms in Support Vector Machines](http://pages.cs.wisc.edu/~swright/talks/sjw-complearning.pdf)
+- [Optimization Algorithms in Support Vector Machines](https://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)
+- [The Interplay of Optimization and Machine Learning Research](https://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/)
+- [Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters](https://vooban.com/en/tips-articles-geek-stuff/hyperopt-tutorial-for-optimizing-neural-networks-hyperparameters/)