Machine Learning & Deep Learning Tutorials

• This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.
• If you want to contribute to this list, please read Contributing Guidelines.
• Curated list of R tutorials for Data Science, NLP and Machine Learning.
• Curated list of Python tutorials for Data Science, NLP and Machine Learning.

Contents

• Introduction
• Interview Resources
• Artificial Intelligence
• Genetic Algorithms
• Statistics
• Useful Blogs
• Resources on Quora
• Resources on Kaggle
• Cheat Sheets
• Classification
• Linear Regression
• Logistic Regression
• Model Validation using Resampling
  • Cross Validation
  • Bootstraping
• Deep Learning
  • Frameworks
  • Feed Forward Networks
  • Recurrent Neural Nets, LSTM, GRU
  • Restricted Boltzmann Machine, DBNs
  • Autoencoders
  • Convolutional Neural Nets
• Natural Language Processing
  • Topic Modeling, LDA
  • Word2Vec
• Computer Vision
• Support Vector Machine
• Reinforcement Learning
• Decision Trees
• Random Forest / Bagging
• Boosting
• Ensembles
• Stacking Models
• VC Dimension
• Bayesian Machine Learning
• Semi Supervised Learning
• Optimizations
• Other Useful Tutorials

Introduction

• Machine Learning Course by Andrew Ng (Stanford University)
• 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

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?
• The Big List of DS/ML Interview Resources

Artificial Intelligence

• Awesome Artificial Intelligence (GitHub Repo)
• UC Berkeley CS188 Intro to AI, Lecture Videos, 2
• Programming Community Curated Resources for learning Artificial Intelligence
• 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
• 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

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
• ML Compiled

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
  • 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
• Residual Analysis
  • Interpreting plot.lm() in R
  • How to interpret a QQ plot?
  • Interpreting Residuals vs Fitted Plot
• Outliers
  • How should outliers be dealt with?
• Elastic Net
  • Regularization and Variable Selection via the Elastic Net

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

• Resampling Explained
• Partioning data set in R
• Implementing hold-out Validaion in R, 2

• 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
  • 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

• Bootstrapping
  • Why Bootstrapping Works?
  • Good Animation
  • Example of Bootstapping
  • Understanding Bootstapping for Validation and Model Selection
  • Cross Validation vs Bootstrap to estimate prediction error, Cross-validation vs .632 bootstrapping to evaluate classification performance

Deep Learning

• 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), 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
• 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
• Neural Machine Translation
  • Machine Translation Reading List
  • Introduction to Neural Machine Translation with GPUs (part 1), Part 2, Part 3
  • Deep Speech: Accurate Speech Recognition with GPU-Accelerated Deep Learning

• 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
    • Android TensorFlow Machine Learning Example
      • GitHub Repo
    • Creating Custom Model For Android Using TensorFlow
      • GitHub Repo

• 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
    • LSTM for Human Activity Recognition
  • Gated Recurrent Units (GRU)
    • 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

• 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
• 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
  • 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
  • Multilingual Latent Dirichlet Allocation (LDA). (Tutorial here)

• 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
• Text Clustering
  • How string clustering works
  • Levenshtein distance for measuring the difference between two sequences
  • Text clustering with Levenshtein distances
• Text Classification
  • Classification Text with Bag of Words
• Named Entity Recognitation
  • 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, Part 2, Part 3
• What would Shakespeare say (NLP Tutorial)
• A closer look at Skip Gram Modeling

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?
• 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
  • 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
• xgboost
  • xgboost tuning kaggle
  • xgboost vs gbm
  • xgboost survey
  • Practical XGBoost in Python online course (free)
• AdaBoost
  • AdaBoost Wiki, Python Code
  • AdaBoost Sparse Input Support
  • adaBag R package
  • Tutorial

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
• 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
• Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters

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.