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
  • Graph Representation Learning
• 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

• 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

• 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, 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 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

• 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](https://speakerdeck.com/jakevdp/statistics-for-hackers) - 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

• 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 (related video)

• An introduction to ROC analysis

• Simple guide to confusion matrix terminology

• 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 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

• 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

• 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

• Network Representation Learning

  • 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, 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
  • Topic Modeling Wikipedia

  • Probabilistic Topic Models Princeton PDF

  • LDA Wikipedia, LSA Wikipedia, Probabilistic LSA Wikipedia

  • What is a good explanation of Latent Dirichlet Allocation (LDA)?

  • Introduction to LDA, Another good explanation

  • 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 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)

  • Deep Belief Nets for Topic Modeling
  • Gaussian LDA for Topic Models with Word Embeddings
  • Python
    • Series of lecture notes for probabilistic topic models written in ipython notebook
    • Implementation of various topic models in Python

• 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

• 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, 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

• Awesome Reinforcement Learning (GitHub)

• RL Tutorial Part 1, 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, 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

• 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 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

• CatBoost

  • 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, 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, 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, Slides

• 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, 2, 3

• 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

• 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.