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##Table of Contents

##Miscellaneous - [Machine Learning for Software Engineers]( - [Dive into Machine Learning]( - [A curated list of awesome Machine Learning frameworks, libraries and software]( - [A curated list of awesome data visualization libraries and resources.]( - [An awesome Data Science repository to learn and apply for real world problems]( - [The Open Source Data Science Masters]( - [Machine Learning FAQs on Cross Validated]( - [List of Machine Learning University Courses]( - [Machine Learning algorithms that you should always have a strong understanding of]( - [Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables]( - [List of Machine Learning Concepts]( - [Slides on Several Machine Learning Topics]( - [MIT Machine Learning Lecture Slides]( - [Comparison Supervised Learning Algorithms]( - [Learning Data Science Fundamentals]( - [Machine Learning mistakes to avoid]( - [Statistical Machine Learning Course]( - [TheAnalyticsEdge edX Notes and Codes]( - [In-depth introduction to machine learning in 15 hours of expert videos]( - [Have Fun With Machine Learning]( ##Interview Resources - [41 Essential Machine Learning Interview Questions (with answers)]( - [How can a computer science graduate student prepare himself for data scientist interviews?]( - [How do I learn Machine Learning?]( - [FAQs about Data Science Interviews]( - [What are the key skills of a data scientist?]( ##Artificial Intelligence - [Awesome Artificial Intelligence (GitHub Repo)]( - [UC Berkeley CS188 Intro to AI](, [Lecture Videos](, [2]( - [MIT 6.034 Artificial Intelligence Lecture Videos](, [Complete Course]( - [edX course | Klein & Abbeel]( - [Udacity Course | Norvig & Thrun]( - [TED talks on AI]( ##Genetic Algorithms - [Genetic Algorithms Wikipedia Page]( - [Simple Implementation of Genetic Algorithms in Python (Part 1)](, [Part 2]( - [Genetic Algorithms vs Artificial Neural Networks]( - [Genetic Algorithms Explained in Plain English]( - [Genetic Programming]( - [Genetic Programming in Python (GitHub)]( - [Genetic Alogorithms vs Genetic Programming (Quora)](, [StackOverflow]( ##Statistics - [Stat Trek Website]( - A dedicated website to teach yourselves Statistics - [Learn Statistics Using Python]( - Learn Statistics using an application-centric programming approach - [Statistics for Hackers | Slides | @jakevdp]( - Slides by Jake VanderPlas - [Online Statistics Book]( - An Interactive Multimedia Course for Studying Statistics - [What is a Sampling Distribution?]( - Tutorials - [AP Statistics Tutorial]( - [Statistics and Probability Tutorial]( - [Matrix Algebra Tutorial]( - [What is an Unbiased Estimator?]( - [Goodness of Fit Explained]( - [What are QQ Plots?]( - [OpenIntro Statistics]( - Free PDF textbook ##Useful Blogs - [Edwin Chen's Blog]( - A blog about Math, stats, ML, crowdsourcing, data science - [The Data School Blog]( - Data science for beginners! - [ML Wave]( - A blog for Learning Machine Learning - [Andrej Karpathy]( - A blog about Deep Learning and Data Science in general - [Colah's Blog]( - Awesome Neural Networks Blog - [Alex Minnaar's Blog]( - A blog about Machine Learning and Software Engineering - [Statistically Significant]( - Andrew Landgraf's Data Science Blog - [Simply Statistics]( - A blog by three biostatistics professors - [Yanir Seroussi's Blog]( - A blog about Data Science and beyond - [fastML]( - Machine learning made easy - [Trevor Stephens Blog]( - Trevor Stephens Personal Page - [no free hunch | kaggle]( - The Kaggle Blog about all things Data Science - [A Quantitative Journey | outlace]( - learning quantitative applications - [r4stats]( - analyze the world of data science, and to help people learn to use R - [Variance Explained]( - David Robinson's Blog - [AI Junkie]( - a blog about Artificial Intellingence - [Deep Learning Blog by Tim Dettmers]( Making deep learning accessible - [J Alammar's Blog]( Blog posts about Machine Learning and Neural Nets - [Adam Geitgey]( - Easiest Introduction to machine learning ##Resources on Quora - [Most Viewed Machine Learning writers]( - [Data Science Topic on Quora]( - [William Chen's Answers]( - [Michael Hochster's Answers]( - [Ricardo Vladimiro's Answers]( - [Storytelling with Statistics]( - [Data Science FAQs on Quora]( - [Machine Learning FAQs on Quora]( ##Kaggle Competitions WriteUp - [How to almost win Kaggle Competitions]( - [Convolution Neural Networks for EEG detection]( - [Facebook Recruiting III Explained]( - [Predicting CTR with Online ML]( - [How to Rank 10% in Your First Kaggle Competition]( ##Cheat Sheets - [Probability Cheat Sheet](, [Source]( - [Machine Learning Cheat Sheet]( ##Classification - [Does Balancing Classes Improve Classifier Performance?]( - [What is Deviance?]( - [When to choose which machine learning classifier?]( - [What are the advantages of different classification algorithms?]( - [ROC and AUC Explained]( ([related video]( - [An introduction to ROC analysis]( - [Simple guide to confusion matrix terminology]( ##Linear Regression - [General](#general-) - [Assumptions of Linear Regression](, [Stack Exchange]( - [Linear Regression Comprehensive Resource]( - [Applying and Interpreting Linear Regression]( - [What does having constant variance in a linear regression model mean?]( - [Difference between linear regression on y with x and x with y]( - [Is linear regression valid when the dependant variable is not normally distributed?]( - Multicollinearity and VIF - [Dummy Variable Trap | Multicollinearity]( - [Dealing with multicollinearity using VIFs]( ##Logistic Regression - [Logistic Regression Wiki]( - [Geometric Intuition of Logistic Regression]( - [Obtaining predicted categories (choosing threshold)]( - [Residuals in logistic regression]( - [Difference between logit and probit models](, [Logistic Regression Wiki](, [Probit Model Wiki]( - [Pseudo R2 for Logistic Regression](, [How to calculate](, [Other Details]( - [Guide to an in-depth understanding of logistic regression]( ##Model Validation using Resampling - [Cross Validation]( - [Training with Full dataset after CV?]( - [Which CV method is best?]( - [Variance Estimates in k-fold CV]( - [Is CV a subsitute for Validation Set?]( - [Choice of k in k-fold CV]( - [CV for ensemble learning]( - [k-fold CV in R]( - [Good Resources]( - Overfitting and Cross Validation - [Preventing Overfitting the Cross Validation Data | Andrew Ng]( - [Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation]( - [CV for detecting and preventing Overfitting]( - [How does CV overcome the Overfitting Problem](
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##Deep Learning - [A curated list of awesome Deep Learning tutorials, projects and communities]( - [Lots of Deep Learning Resources]( - [Interesting Deep Learning and NLP Projects (Stanford)](, [Website]( - [Core Concepts of Deep Learning]( - [Understanding Natural Language with Deep Neural Networks Using Torch]( - [Stanford Deep Learning Tutorial]( - [Deep Learning FAQs on Quora]( - [Google+ Deep Learning Page]( - [Recent Reddit AMAs related to Deep Learning](, [Another AMA]( - [Where to Learn Deep Learning?]( - [Deep Learning nvidia concepts]( - [Introduction to Deep Learning Using Python (GitHub)](, [Good Introduction Slides]( - [Video Lectures Oxford 2015](, [Video Lectures Summer School Montreal]( - [Deep Learning Software List]( - [Hacker's guide to Neural Nets]( - [Top arxiv Deep Learning Papers explained]( - [Geoff Hinton Youtube Vidoes on Deep Learning]( - [Awesome Deep Learning Reading List]( - [Deep Learning Comprehensive Website](, [Software]( - [deeplearning Tutorials]( - [AWESOME! Deep Learning Tutorial]( - [Deep Learning Basics]( - [Stanford Tutorials]( - [Train, Validation & Test in Artificial Neural Networks]( - [Artificial Neural Networks Tutorials]( - [Neural Networks FAQs on Stack Overflow]( - [Deep Learning Tutorials on]( - [Neural Networks and Deep Learning Online Book]( - Deep Learning Frameworks - [Torch vs. Theano]( - [dl4j vs. torch7 vs. theano]( - [Deep Learning Libraries by Language](
- [Theano](
    - [Website](
    - [Theano Introduction](
    - [Theano Tutorial](
    - [Good Theano Tutorial](
    - [Logistic Regression using Theano for classifying digits](
    - [MLP using Theano](
    - [CNN using Theano](
    - [RNNs using Theano](
    - [LSTM for Sentiment Analysis in Theano](
    - [RBM using Theano](
    - [DBNs using Theano](
    - [All Codes](
    - [Deep Learning Implementation Tutorials - Keras and Lasagne](

- [Torch](
    - [Torch ML Tutorial](, [Code](
    - [Intro to Torch](
    - [Learning Torch GitHub Repo](
    - [Awesome-Torch (Repository on GitHub)](
    - [Machine Learning using Torch Oxford Univ](, [Code](
    - [Torch Internals Overview](
    - [Torch Cheatsheet](
    - [Understanding Natural Language with Deep Neural Networks Using Torch](

- Caffe
    - [Deep Learning for Computer Vision with Caffe and cuDNN](

- TensorFlow
    - [Website](
    - [TensorFlow Examples for Beginners](
    - [Stanford Tensorflow for Deep Learning Research Course](
        - [GitHub Repo](
    - [Simplified Scikit-learn Style Interface to TensorFlow](
    - [Learning TensorFlow GitHub Repo](
    - [Benchmark TensorFlow GitHub](
    - [Awesome TensorFlow List](
    - [TensorFlow Book](
- Feed Forward Networks - [A Quick Introduction to Neural Networks]( - [Implementing a Neural Network from scratch](, [Code]( - [Speeding up your Neural Network with Theano and the gpu](, [Code]( - [Basic ANN Theory]( - [Role of Bias in Neural Networks]( - [Choosing number of hidden layers and nodes](,[2](,[3]( - [Backpropagation in Matrix Form]( - [ANN implemented in C++ | AI Junkie]( - [Simple Implementation]( - [NN for Beginners]( - [Regression and Classification with NNs (Slides)]( - [Another Intro]( - Recurrent and LSTM Networks - [awesome-rnn: list of resources (GitHub Repo)]( - [Recurrent Neural Net Tutorial Part 1](, [Part 2] (, [Part 3] (, [Code]( - [NLP RNN Representations]( - [The Unreasonable effectiveness of RNNs](, [Torch Code](, [Python Code]( - [Intro to RNN](, [LSTM]( - [An application of RNN]( - [Optimizing RNN Performance]( - [Simple RNN]( - [Auto-Generating Clickbait with RNN]( - [Sequence Learning using RNN (Slides)]( - [Machine Translation using RNN (Paper)]( - [Music generation using RNNs (Keras)]( - [Using RNN to create on-the-fly dialogue (Keras)]( - Long Short Term Memory (LSTM) - [Understanding LSTM Networks]( - [LSTM explained]( - [Beginner’s Guide to LSTM]( - [Implementing LSTM from scratch](, [Python/Theano code]( - [Torch Code for character-level language models using LSTM]( - [LSTM for Kaggle EEG Detection competition (Torch Code)]( - [LSTM for Sentiment Analysis in Theano]( - [Deep Learning for Visual Q&A | LSTM | CNN](, [Code]( - [Computer Responds to email using LSTM | Google]( - [LSTM dramatically improves Google Voice Search](, [Another Article]( - [Understanding Natural Language with LSTM Using Torch]( - [Torch code for Visual Question Answering using a CNN+LSTM model]( - Gated Recurrent Units (GRU) - [LSTM vs GRU]( - [Recursive Neural Network (not Recurrent)]( - [Recursive Neural Tensor Network (RNTN)]( - [word2vec, DBN, RNTN for Sentiment Analysis ]( - Restricted Boltzmann Machine - [Beginner's Guide about RBMs]( - [Another Good Tutorial]( - [Introduction to RBMs]( - [Hinton's Guide to Training RBMs]( - [RBMs in R]( - [Deep Belief Networks Tutorial]( - [word2vec, DBN, RNTN for Sentiment Analysis ]( - Autoencoders: Unsupervised (applies BackProp after setting target = input) - [Andrew Ng Sparse Autoencoders pdf]( - [Deep Autoencoders Tutorial]( - [Denoising Autoencoders](, [Theano Code]( - [Stacked Denoising Autoencoders]( - Convolutional Neural Networks - [An Intuitive Explanation of Convolutional Neural Networks]( - [Awesome Deep Vision: List of Resources (GitHub)]( - [Intro to CNNs]( - [Understanding CNN for NLP]( - [Stanford Notes](, [Codes](, [GitHub]( - [JavaScript Library (Browser Based) for CNNs]( - [Using CNNs to detect facial keypoints]( - [Deep learning to classify business photos at Yelp]( - [Interview with Yann LeCun | Kaggle]( - [Visualising and Understanding CNNs]( ##Natural Language Processing - [A curated list of speech and natural language processing resources]( - [Understanding Natural Language with Deep Neural Networks Using Torch]( - [tf-idf explained]( - [Interesting Deep Learning NLP Projects Stanford](, [Website]( - [NLP from Scratch | Google Paper]( - [Graph Based Semi Supervised Learning for NLP]( - [Bag of Words]( - [Classification text with Bag of Words]( - [Topic Modeling]( - [LDA](, [LSA](, [Probabilistic LSA]( - [What is a good explanation of Latent Dirichlet Allocation?]( - [Awesome LDA Explanation!]( [Another good explanation]( - [The LDA Buffet- Intuitive Explanation]( - [Difference between LSI and LDA]( - [Original LDA Paper]( - [alpha and beta in LDA]( - [Intuitive explanation of the Dirichlet distribution]( - [Topic modeling made just simple enough]( - [Online LDA](, [Online LDA with Spark]( - [LDA in Scala](, [Part 2]( - [Segmentation of Twitter Timelines via Topic Modeling]( - [Topic Modeling of Twitter Followers]( - word2vec - [Google word2vec]( - [Bag of Words Model Wiki]( - [word2vec Tutorial]( - [A closer look at Skip Gram Modeling]( - [Skip Gram Model Tutorial](, [CBoW Model]( - [Word Vectors Kaggle Tutorial Python](, [Part 2]( - [Making sense of word2vec]( - [word2vec explained on deeplearning4j]( - [Quora word2vec]( - [Other Quora Resources](, [2](, [3]( - [word2vec, DBN, RNTN for Sentiment Analysis ]( ##Computer Vision - [Awesome computer vision (github)]( - [Awesome deep vision (github)]( ##Support Vector Machine - [Highest Voted Questions about SVMs on Cross Validated]( - [Help me Understand SVMs!]( - [SVM in Layman's terms]( - [How does SVM Work | Comparisons]( - [A tutorial on SVMs]( - [Practical Guide to SVC](, [Slides]( - [Introductory Overview of SVMs]( - Comparisons - [SVMs > ANNs](, [ANNs > SVMs](, [Another Comparison]( - [Trees > SVMs]( - [Kernel Logistic Regression vs SVM]( - [Logistic Regression vs SVM](, [2](, [3]( - [Optimization Algorithms in Support Vector Machines]( - [Variable Importance from SVM]( - Software - [LIBSVM]( - [Intro to SVM in R]( - Kernels - [What are Kernels in ML and SVM?]( - [Intuition Behind Gaussian Kernel in SVMs?]( - Probabilities post SVM - [Platt's Probabilistic Outputs for SVM]( - [Platt Calibration Wiki]( - [Why use Platts Scaling]( - [Classifier Classification with Platt's Scaling]( ##Reinforcement Learning - [Awesome Reinforcement Learning (GitHub)]( - [RL Tutorial Part 1](, [Part 2]( ##Decision Trees - [Wikipedia Page - Lots of Good Info]( - [FAQs about Decision Trees]( - [Brief Tour of Trees and Forests]( - [Tree Based Models in R]( - [How Decision Trees work?]( - [Weak side of Decision Trees]( - [Thorough Explanation and different algorithms]( - [What is entropy and information gain in the context of building decision trees?]( - [Slides Related to Decision Trees]( - [How do decision tree learning algorithms deal with missing values?]( - [Using Surrogates to Improve Datasets with Missing Values]( - [Good Article]( - [Are decision trees almost always binary trees?]( - [Pruning Decision Trees](, [Grafting of Decision Trees]( - [What is Deviance in context of Decision Trees?]( - Comparison of Different Algorithms - [CART vs CTREE]( - [Comparison of complexity or performance]( - [CHAID vs CART]( , [CART vs CHAID]( - [Good Article on comparison]( - CART - [Recursive Partitioning Wikipedia]( - [CART Explained]( - [How to measure/rank “variable importance” when using CART?]( - [Pruning a Tree in R]( - [Does rpart use multivariate splits by default?]( - [FAQs about Recursive Partitioning]( - CTREE - [party package in R]( - [Show volumne in each node using ctree in R]( - [How to extract tree structure from ctree function?]( - CHAID - [Wikipedia Artice on CHAID]( - [Basic Introduction to CHAID]( - [Good Tutorial on CHAID]( - MARS - [Wikipedia Article on MARS]( - Probabilistic Decision Trees - [Bayesian Learning in Probabilistic Decision Trees]( - [Probabilistic Trees Research Paper]( ##Random Forest / Bagging - [Awesome Random Forest (GitHub)**]( - [How to tune RF parameters in practice?]( - [Measures of variable importance in random forests]( - [Compare R-squared from two different Random Forest models]( - [OOB Estimate Explained | RF vs LDA]( - [Evaluating Random Forests for Survival Analysis Using Prediction Error Curve]( - [Why doesn't Random Forest handle missing values in predictors?]( - [How to build random forests in R with missing (NA) values?]( - [FAQs about Random Forest](, [More FAQs]( - [Obtaining knowledge from a random forest]( - [Some Questions for R implementation](, [2](, [3]( ##Boosting - [Boosting for Better Predictions]( - [Boosting Wikipedia Page]( - [Introduction to Boosted Trees | Tianqi Chen]( - Gradient Boosting Machine - [Gradiet Boosting Wiki]( - [Guidelines for GBM parameters in R](, [Strategy to set parameters]( - [Meaning of Interaction Depth](, [2]( - [Role of n.minobsinnode parameter of GBM in R]( - [GBM in R]( - [FAQs about GBM]( - [GBM vs xgboost]( ##Ensembles - [Wikipedia Article on Ensemble Learning]( - [Kaggle Ensembling Guide]( - [The Power of Simple Ensembles]( - [Ensemble Learning Intro]( - [Ensemble Learning Paper]( - [Ensembling models with R](, [Ensembling Regression Models in R](, [Intro to Ensembles in R]( - [Ensembling Models with caret]( - [Bagging vs Boosting vs Stacking]( - [Good Resources | Kaggle Africa Soil Property Prediction]( - [Boosting vs Bagging]( - [Resources for learning how to implement ensemble methods]( - [How are classifications merged in an ensemble classifier?]( ##Stacking Models - [Stacking, Blending and Stacked Generalization]( - [Stacked Generalization (Stacking)]( - [Stacked Generalization: when does it work?]( - [Stacked Generalization Paper]( ##Vapnik–Chervonenkis Dimension - [Wikipedia article on VC Dimension]( - [Intuitive Explanantion of VC Dimension]( - [Video explaining VC Dimension]( - [Introduction to VC Dimension]( - [FAQs about VC Dimension]( - [Do ensemble techniques increase VC-dimension?]( ##Bayesian Machine Learning - [Bayesian Methods for Hackers (using pyMC)]( - [Should all Machine Learning be Bayesian?]( - [Tutorial on Bayesian Optimisation for Machine Learning]( - [Bayesian Reasoning and Deep Learning](, [Slides]( - [Bayesian Statistics Made Simple]( - [Kalman & Bayesian Filters in Python]( - [Markov Chain Wikipedia Page]( ##Semi Supervised Learning - [Wikipedia article on Semi Supervised Learning]( - [Tutorial on Semi Supervised Learning]( - [Graph Based Semi Supervised Learning for NLP]( - [Taxonomy]([0].pdf) - [Video Tutorial Weka]( - [Unsupervised, Supervised and Semi Supervised learning]( - [Research Papers 1](, [2](, [3]( ##Optimization - [Mean Variance Portfolio Optimization with R and Quadratic Programming]( - [Algorithms for Sparse Optimization and Machine Learning]( - [Optimization Algorithms in Machine Learning](, [Video Lecture]( - [Optimization Algorithms for Data Analysis]( - [Video Lectures on Optimization]( - [Optimization Algorithms in Support Vector Machines]( - [The Interplay of Optimization and Machine Learning Research]( ##Other Tutorials - For a collection of Data Science Tutorials using R, please refer to [this list]( - For a collection of Data Science Tutorials using Python, please refer to [this list](
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