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An open API service indexing awesome lists of open source software.
Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
https://github.com/ujjwalkarn/Machine-Learning-Tutorials
- list
- Curated list of R tutorials for Data Science, NLP and Machine Learning
- Curated list of Python tutorials for Data Science, NLP and Machine Learning
- Machine Learning Course by Andrew Ng (Stanford University)
- AI/ML YouTube Courses
- Curated List of Machine Learning Resources
- In-depth introduction to machine learning in 15 hours of expert videos
- An Introduction to Statistical Learning
- List of Machine Learning University Courses
- 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
- 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
- Have Fun With Machine Learning
- Twitter's Most Shared #machineLearning Content From The Past 7 Days
- Grokking Machine Learning
- 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?
- The Big List of DS/ML Interview Resources
- Awesome Artificial Intelligence (GitHub Repo)
- UC Berkeley CS188 Intro to AI - HSakPTM)
- Programming Community Curated Resources for learning Artificial Intelligence
- MIT 6.034 Artificial Intelligence Lecture Videos - engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)
- edX course | Klein & Abbeel
- Udacity Course | Norvig & Thrun
- TED talks on AI
- Genetic Algorithms Wikipedia Page
- Simple Implementation of Genetic Algorithms in Python (Part 1)
- 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) - are-the-differences-between-genetic-algorithms-and-genetic-programming)
- 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?
- 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
- 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
- Ethen's Notebook Collection - Continuously updated machine learning documentations (mainly in Python3). Contents include educational implementation of machine learning algorithms from scratch and open-source library usage
- 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
- 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
- Probability Cheat Sheet
- Source
- Machine Learning Cheat Sheet
- ML Compiled
- 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 - yo))
- An introduction to ROC analysis
- Simple guide to confusion matrix terminology
- Assumptions of Linear Regression - is-a-complete-list-of-the-usual-assumptions-for-linear-regression)
- 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?
- Dummy Variable Trap | Multicollinearity
- Dealing with multicollinearity using VIFs
- Interpreting plot.lm() in R
- How to interpret a QQ plot?
- Interpreting Residuals vs Fitted Plot
- How should outliers be dealt with?
- Elastic Net
- Logistic Regression Wiki
- Geometric Intuition of Logistic Regression
- Obtaining predicted categories (choosing threshold)
- Residuals in logistic regression
- Difference between logit and probit models
- Pseudo R2 for Logistic Regression - to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm)
- Guide to an in-depth understanding of logistic regression
- Resampling Explained
- Partioning data set in R
- Implementing hold-out Validaion in R - data-frame-into-testing-and.html)
- Cross Validation
- How to use cross-validation in predictive modeling
- 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
- 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
- Bootstrapping
- Why Bootstrapping Works?
- Good Animation
- Example of Bootstapping
- Understanding Bootstapping for Validation and Model Selection
- Cross Validation vs Bootstrap to estimate prediction error - validation vs .632 bootstrapping to evaluate classification performance](http://stats.stackexchange.com/questions/71184/cross-validation-or-bootstrapping-to-evaluate-classification-performance)
- fast.ai - Practical Deep Learning For Coders
- fast.ai - Cutting Edge Deep Learning For Coders
- A curated list of awesome Deep Learning tutorials, projects and communities
- Deep Learning Papers Reading Roadmap
- Lots of Deep Learning Resources
- Interesting Deep Learning and NLP Projects (Stanford)
- 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
- Where to Learn Deep Learning?
- Deep Learning nvidia concepts
- Introduction to Deep Learning Using Python (GitHub) - to-deep-learning)
- Video Lectures Oxford 2015
- 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
- deeplearning Tutorials
- AWESOME! Deep Learning Tutorial
- Deep Learning Basics
- Intuition Behind Backpropagation
- Stanford Tutorials
- Train, Validation & Test in Artificial Neural Networks
- Artificial Neural Networks Tutorials
- Neural Networks FAQs on Stack Overflow
- Deep Learning Tutorials on deeplearning.net
- Neural Networks and Deep Learning Online Book
- Machine Translation Reading List
- Introduction to Neural Machine Translation with GPUs (part 1) - neural-machine-translation-gpus-part-2/), [Part 3](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/)
- Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning
- 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
- Intro to Torch
- Learning Torch GitHub Repo
- Awesome-Torch (Repository on GitHub)
- Machine Learning using Torch Oxford Univ - cs-ml-2015)
- Torch Internals Overview
- Torch Cheatsheet
- Understanding Natural Language with Deep Neural Networks Using Torch
- Deep Learning for Computer Vision with Caffe and cuDNN
- 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
- Android TensorFlow Machine Learning Example
- GitHub Repo
- Creating Custom Model For Android Using TensorFlow
- GitHub Repo
- A Quick Introduction to Neural Networks
- Implementing a Neural Network from scratch - from-scratch)
- Speeding up your Neural Network with Theano and the gpu - theano)
- Basic ANN Theory
- Role of Bias in Neural Networks
- Choosing number of hidden layers and nodes - layer-perceptron-mlp-architecture-criteria-for-choosing-number-of-hidde?lq=1),[3](http://stackoverflow.com/questions/9436209/how-to-choose-number-of-hidden-layers-and-nodes-in-neural-network/2#)
- Backpropagation in Matrix Form
- ANN implemented in C++ | AI Junkie
- Simple Implementation
- NN for Beginners
- Regression and Classification with NNs (Slides)
- Another Intro
- awesome-rnn: list of resources (GitHub Repo)
- Recurrent Neural Net Tutorial Part 1 - neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/), [Part 3](http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/), [Code](https://github.com/dennybritz/rnn-tutorial-rnnlm/)
- NLP RNN Representations
- The Unreasonable effectiveness of RNNs - rnn), [Python Code](https://gist.github.com/karpathy/d4dee566867f8291f086)
- Intro to RNN
- 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)
- Understanding LSTM Networks
- LSTM explained
- Beginner’s Guide to LSTM
- Implementing LSTM from scratch - tutorial-gru-lstm)
- 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 - qa)
- Computer Responds to email using LSTM | Google
- LSTM dramatically improves Google Voice Search - short-term-memory-dramatically-improves-google-voice-etc-now-available-to-a-billion-users/)
- Understanding Natural Language with LSTM Using Torch
- Torch code for Visual Question Answering using a CNN+LSTM model
- LSTM for Human Activity Recognition
- LSTM vs GRU
- Time series forecasting with Sequence-to-Sequence (seq2seq) rnn models
- Recursive Neural Network (not Recurrent)
- Recursive Neural Tensor Network (RNTN)
- word2vec, DBN, RNTN for Sentiment Analysis
- 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
- Andrew Ng Sparse Autoencoders pdf
- Deep Autoencoders Tutorial
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- An Intuitive Explanation of Convolutional Neural Networks
- Awesome Deep Vision: List of Resources (GitHub)
- Intro to CNNs
- Understanding CNN for NLP
- Stanford Notes
- 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
- Awesome Graph Embedding
- Awesome Network Embedding
- Network Representation Learning Papers
- Knowledge Representation Learning Papers
- Graph Based Deep Learning Literature
- 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
- The Stanford NLP Group
- NLP from Scratch | Google Paper
- Graph Based Semi Supervised Learning for NLP
- Bag of Words
- Classification text with Bag of Words
- Topic Modeling Wikipedia
- **Probabilistic Topic Models Princeton PDF**
- LDA Wikipedia
- What is a good explanation of Latent Dirichlet Allocation (LDA)?
- **Introduction to LDA** - boyd-graber-and-philip-resnik.html)
- The LDA Buffet - Intuitive Explanation
- Your Guide to Latent Dirichlet Allocation (LDA)
- Difference between LSI and LDA
- Original LDA Paper
- alpha and beta in LDA
- Intuitive explanation of the Dirichlet distribution
- topicmodels: An R Package for Fitting Topic Models
- Topic modeling made just simple enough
- Online LDA - online-latent-dirichlet-allocation-with-apache-spark.html)
- LDA in Scala - dirichlet-allocation-in-scala-part-ii-the-code.html)
- Segmentation of Twitter Timelines via Topic Modeling
- Topic Modeling of Twitter Followers
- Multilingual Latent Dirichlet Allocation (LDA) - Latent-Dirichlet-Allocation-LDA/blob/master/Multilingual-LDA-Pipeline-Tutorial.ipynb))
- Deep Belief Nets for Topic Modeling
- Gaussian LDA for Topic Models with Word Embeddings
- Series of lecture notes for probabilistic topic models written in ipython notebook
- Implementation of various topic models in Python
- Google word2vec
- Bag of Words Model Wiki
- word2vec Tutorial
- A closer look at Skip Gram Modeling
- Skip Gram Model Tutorial - tutorial-part-ii-the-continuous-bag-of-words-model.html)
- Word Vectors Kaggle Tutorial Python - nlp-tutorial/details/part-3-more-fun-with-word-vectors)
- Making sense of word2vec
- word2vec explained on deeplearning4j
- Quora word2vec
- Other Quora Resources - 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
- How string clustering works
- Levenshtein distance for measuring the difference between two sequences
- Text clustering with Levenshtein distances
- Classification Text with Bag of Words
- Stanford Named Entity Recognizer (NER)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Language learning with NLP and reinforcement learning
- Kaggle Tutorial Bag of Words and Word vectors - 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)
- A closer look at Skip Gram Modeling
- Awesome computer vision (github)
- Awesome deep vision (github)
- 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
- Introductory Overview of SVMs
- SVMs > ANNs - are-advantages-of-artificial-neural-networks-over-support-vector-machines), [Another Comparison](http://www.svms.org/anns.html)
- Trees > SVMs
- Kernel Logistic Regression vs SVM
- Logistic Regression vs 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
- Variable Importance from SVM
- LIBSVM
- Intro to SVM in R
- What are Kernels in ML and SVM?
- Intuition Behind Gaussian Kernel in SVMs?
- Platt's Probabilistic Outputs for SVM
- Platt Calibration Wiki
- Why use Platts Scaling
- Classifier Classification with Platt's Scaling
- Awesome Reinforcement Learning (GitHub)
- RL Tutorial Part 1 - Learning-Part-2/)
- 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
- What is Deviance in context of Decision Trees?
- Discover structure behind data with decision trees - Grow and plot a decision tree to automatically figure out hidden rules in your data
- CART vs CTREE
- Comparison of complexity or performance
- CHAID vs CART - trees-cart-vs-chaid.html)
- Good Article on comparison
- 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
- party package in R
- Show volumne in each node using ctree in R
- How to extract tree structure from ctree function?
- Wikipedia Artice on CHAID
- Basic Introduction to CHAID
- Good Tutorial on CHAID
- Wikipedia Article on MARS
- Bayesian Learning in Probabilistic Decision Trees
- Probabilistic Trees Research Paper
- 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 - forest)
- Obtaining knowledge from a random forest
- Some Questions for R implementation - 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)
- Boosting for Better Predictions
- Boosting Wikipedia Page
- Introduction to Boosted Trees | Tianqi Chen
- Gradiet Boosting Wiki
- Guidelines for GBM parameters in R - to-set-the-gbm-parameters)
- Meaning of Interaction Depth - does-interaction-depth-mean-in-gbm)
- Role of n.minobsinnode parameter of GBM in R
- GBM in R
- FAQs about GBM
- GBM vs xgboost
- xgboost tuning kaggle
- xgboost vs gbm
- xgboost survey
- Practical XGBoost in Python online course (free)
- AdaBoost Wiki
- AdaBoost Sparse Input Support
- adaBag R package
- Tutorial
- CatBoost Documentation
- Benchmarks
- Tutorial
- GitHub Project
- CatBoost vs. Light GBM vs. XGBoost
- Wikipedia Article on Ensemble Learning
- Kaggle Ensembling Guide
- The Power of Simple Ensembles
- Ensemble Learning Intro
- Ensemble Learning Paper
- Ensembling models with R - regression-models), [Intro to Ensembles in R](http://www.vikparuchuri.com/blog/intro-to-ensemble-learning-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, Blending and Stacked Generalization
- Stacked Generalization (Stacking)
- Stacked Generalization: when does it work?
- Stacked Generalization Paper
- 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 Methods for Hackers (using pyMC)
- Should all Machine Learning be Bayesian?
- Tutorial on Bayesian Optimisation for Machine Learning
- Bayesian Reasoning and Deep Learning - content/uploads/2015/10/Bayes_Deep.pdf)
- Bayesian Statistics Made Simple
- Kalman & Bayesian Filters in Python
- Markov Chain Wikipedia Page
- Wikipedia article on Semi Supervised Learning
- Tutorial on Semi Supervised Learning
- Graph Based Semi Supervised Learning for NLP
- Taxonomy
- Video Tutorial Weka
- Unsupervised, Supervised and Semi Supervised learning
- Research Papers 1
- Mean Variance Portfolio Optimization with R and Quadratic Programming
- Algorithms for Sparse Optimization and Machine Learning
- Optimization Algorithms in Machine Learning
- Optimization Algorithms for Data Analysis
- Video Lectures on Optimization
- Optimization Algorithms in Support Vector Machines
- The Interplay of Optimization and Machine Learning Research
- Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
- this list
- this list
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