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Machine-Learning-Tutorials

machine learning and deep learning tutorials, articles and other resources
https://github.com/ujjwalkarn/Machine-Learning-Tutorials

Last synced: 2 days ago
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Resources on Quora
- Michael Hochster's Answers
- Machine Learning FAQs on Quora
- Most Viewed Machine Learning writers
- Data Science Topic on Quora
- William Chen's Answers
- Ricardo Vladimiro's Answers
- Storytelling with Statistics
- Data Science FAQs on Quora
- Storytelling with Statistics
Deep Learning
- Torch Cheatsheet
- Deep Autoencoders Tutorial
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- 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
- Network Representation Learning Papers
- 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
- 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
- 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
- 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
- Speeding up your Neural Network with Theano and the gpu - theano)
- 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
- DBNs using Theano
- Torch
- Torch ML Tutorial
- Intro to Torch
- Machine Learning using Torch Oxford Univ - cs-ml-2015)
- Torch Internals Overview
- Deep Learning for Computer Vision with Caffe and cuDNN
- Website
- Stanford Tensorflow for Deep Learning Research Course
- Learning TensorFlow GitHub Repo
- Benchmark TensorFlow GitHub
- Awesome TensorFlow List
- Android TensorFlow Machine Learning Example
- Creating Custom Model For Android Using TensorFlow
- A Quick Introduction to Neural Networks
- Implementing a Neural Network from scratch - from-scratch)
- 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
- 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
- Machine Translation using RNN (Paper)
- Using RNN to create on-the-fly dialogue (Keras)
- Understanding LSTM Networks
- LSTM explained
- Beginner’s Guide to LSTM
- LSTM vs GRU
- Recursive Neural Tensor Network (RNTN)
- 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/)
- Recursive Neural Network (not Recurrent)
- Beginner's Guide about RBMs
- Introduction to RBMs
- Hinton's Guide to Training RBMs
- Deep Belief Networks Tutorial
- Andrew Ng Sparse Autoencoders pdf
- An Intuitive Explanation of Convolutional Neural Networks
- Intro to CNNs
- Another Good Tutorial
- Where to Learn Deep Learning?
- Top arxiv Deep Learning Papers explained
- Deep Learning Implementation Tutorials - Keras and Lasagne
- Sequence Learning using RNN (Slides)
Support Vector Machine
- How does SVM Work | Comparisons
- A tutorial on SVMs
- Practical Guide to SVC
- Introductory Overview of SVMs
- Highest Voted Questions about SVMs on Cross Validated
- Help me Understand SVMs!
- SVM in Layman's terms
- 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)
- 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
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
- 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
- Twitter's Most Shared #machineLearning Content From The Past 7 Days
- Grokking Machine Learning
- A curated list of awesome data visualization libraries and resources.
- Learning Data Science Fundamentals
- AI/ML YouTube Courses
- Machine Learning for Software Engineers
- Dive into Machine Learning
- A curated list of awesome data visualization libraries and resources.
- An awesome Data Science repository to learn and apply for real world problems
- In-depth introduction to machine learning in 15 hours of expert videos
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 - 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
- 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 Alogorithms vs Genetic Programming (Quora) - are-the-differences-between-genetic-algorithms-and-genetic-programming)
Statistics
- Stat Trek Website - A dedicated website to teach yourselves Statistics
- 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
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
Random Forest / Bagging
- FAQs about Random Forest - forest)
- 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?
- 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)
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
- How to Rank 10% in Your First Kaggle Competition
Cheat Sheets
- Probability Cheat Sheet
- Source
- ML Compiled
Classification
- Does Balancing Classes Improve Classifier Performance?
- 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
Decision Trees
- What is Deviance in context of 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?
- 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
- 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
- Slides Related to Decision Trees
Linear Regression
- 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
- 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
Model Validation using Resampling
- 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)
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
- How to use cross-validation in predictive modeling
Natural Language Processing
- word2vec, DBN, RNTN for Sentiment Analysis
- A curated list of speech and natural language processing resources
- Understanding Natural Language with Deep Neural Networks Using Torch
- tf-idf explained
- The Stanford NLP Group
- NLP from Scratch | Google Paper
- Word Vectors Kaggle Tutorial Python - nlp-tutorial/details/part-3-more-fun-with-word-vectors)
- 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)
- 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
- Gaussian LDA for Topic Models with Word Embeddings
- Google word2vec
- word2vec Tutorial
- A closer look at Skip Gram Modeling
- Skip Gram Model Tutorial - tutorial-part-ii-the-continuous-bag-of-words-model.html)
- 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)
- How string clustering works
- Levenshtein distance for measuring the difference between two sequences
- Text clustering with Levenshtein distances
- Stanford Named Entity Recognizer (NER)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Language learning with NLP and reinforcement learning
- What would Shakespeare say (NLP Tutorial)
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Bag of Words Model Wiki
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Classification Text with Bag of Words
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Interesting Deep Learning NLP Projects Stanford
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
- Named Entity Recognition: Applications and Use Cases- Towards Data Science
Computer Vision
- Awesome computer vision (github)
- Awesome deep vision (github)
Optimization
- Optimization Algorithms in Support Vector Machines
- 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
- The Interplay of Optimization and Machine Learning Research
- Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters
Reinforcement Learning
- RL Tutorial Part 1 - Learning-Part-2/)
Boosting
- 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
- GitHub Project
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- xgboost tuning kaggle
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light GBM vs. XGBoost
- CatBoost vs. Light 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 - 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 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
- FAQs about VC Dimension
- Do ensemble techniques increase VC-dimension?
- Introduction to VC Dimension
Bayesian Machine Learning
- 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
- Markov Chain Wikipedia Page
Semi Supervised Learning
- Wikipedia article on Semi Supervised Learning
- Tutorial on Semi Supervised Learning
- Taxonomy
- Video Tutorial Weka
- Unsupervised, Supervised and Semi Supervised learning
- Research Papers 1
- Graph Based Semi Supervised Learning for NLP
Uncategorized
- Uncategorized

Programming Languages

Categories

Deep Learning 113 Natural Language Processing 89 Boosting 66 Model Validation using Resampling 36 Decision Trees 35 Introduction 30 Support Vector Machine 20 Useful Blogs 19 Linear Regression 13 Ensembles 12 Statistics 11 Random Forest / Bagging 10 Resources on Quora 9 Optimization 8 Artificial Intelligence 7 Semi Supervised Learning 7 Logistic Regression 7 Genetic Algorithms 6 Interview Resources 6 Vapnik–Chervonenkis Dimension 6 Kaggle Competitions WriteUp 6 Classification 6 Bayesian Machine Learning 5 Stacking Models 4 Uncategorized 3 Cheat Sheets 3 Computer Vision 2 Reinforcement Learning 1

Sub Categories

Uncategorized 3

Keywords

awesome-list 5 deep-learning 5 machine-learning 5 awesome 3 data-science 3 ai 2 artificial-intelligence 2 data-visualization 2 neural-network 2 data-scientists 2 face-images 1 deep-networks 1 deep-learning-tutorial 1 unsupervised-learning 1 statistical-learning 1 reinforcement-learning 1 machine-intelligence 1 intelligent-systems 1 intelligent-machines 1 courses 1 computer-science 1 science 1 data-mining 1 analytics 1 visualize-data 1 visualization 1 dataviz 1 chart 1 software-engineer 1 machine-learning-algorithms 1 nlp 1 natural-language-processing 1 python-tutorial 1 python 1 unicorns 1 resources 1 lists 1 recurrent-networks 1