List of machine learning resources. Part 2

miscellanea

• The list: excellent frameworks, libraries and machine learning applications;
• List: excellent libraries and other resources for data visualization;
• Awesome Data Science: data science materials;
• Data Science Masters: educational materials and datalogy literature;
• Cross Validated: Machine Learning FAQ;
• List: university courses related to machine learning;
• Quora: machine learning algorithms that need to be understood;
• Article: the difference between linearly independent, orthogonal, and uncorrelated variables;
• References: machine learning concepts and algorithms;
• Presentations: various topics;

• Presentation: MIT Machine Learning Lectures;
• Article: comparison of learning algorithms with a teacher;
• Article: The Basics of Data Science;
• Article: Three machine learning errors to be avoided;
• TheAnalyticsEdge: lectures with examples;

Interview

• Quora: how to prepare a student for an interview for the position of data specialist;
• Quora: where to start familiarity with machine learning;
• Quora: Interview FAQ for a Data Specialist;
• Quora: the most important skills for a data processing specialist;

Artificial Intelligence

• Repository: list of resources on artificial intelligence;
• edX: an artificial intelligence course by Dan Klein and Peter Abbel;
• Udacity: Peter Norvig and Sebastian Trun's course;
• TED Talks: artificial intelligence;

Genetic algorithms

• Wiki: genetic algorithms;
• Outlace: a simple implementation of genetic algorithms in Python (Part 1);
• Outlace: a simple implementation of genetic algorithms in Python (Part 2);
• ai-junkie: about genetic algorithms in simple language;
• Wiki: genetic programming;
• GitHub: genetic programming in Python;
• Quora: genetic algorithms and genetic programming;

Statistics

• Stat Trek: everything about statistics and probabilities;
• Intro2stats: learn statistics with Python;
• Statistics for Hackers: Jake Vanderplas presentation;
• Online Statistics Book: an online multimedia course on statistics;
• Article: What is a selective distribution;
• Training: a program of advanced study of statistics;
• Training: statistics and probability;
• Training: matrix algebra;
• Forum: what is unbiased rating;
• Wiki: acceptance criteria;
• Article: what is quantile-quantile graphics;

Useful blogs

• Edwin Chen's blog : a blog about math, statistics, machine learning, and data science;
• Data School: datalogy for beginners;
• ML Wave: learning machine learning;
• Karpathy: a blog about deep learning and data science;
• Colah: a great blog about neural networks;
• Alex Minaar’s blog : machine learning and programming blog;
• Statistically Significant: Andrew Landgraf's blog on data science;
• Simply Statistics: a blog led by three professors of biostatistics;
• Yanir Seroussi: a data science blog and more;
• fastML: accessible language about machine learning;

• Trevor Stephens: Trevor Stephens Personal Page;
• Kaggle: everything about data processing and analysis;
• Outlace: a student learning blog about machine learning;
• r4stats: everything about data science and R;
• Variance Explained: David Robinson blog;
• AI Junkie: Artificial Intelligence Blog;

Quora Resources

• Quora: the most popular authors who write about machine learning;
• Data science: thematic section;
• Replies from William Chen;
• Answers Michael Hotster;
• Answers Ricardo Vladimiro;
• Blog: useful materials and tips on statistics, not only;
• FAQ: on data science;
• FAQ: machine learning;

Kaggle Competitions

• Article: how to (almost) win the Kaggle Competitions;
• Article: application of convolutional neural networks for decoding EEG signals;
• Article: Facebook Recruiting III Analysis;
• Article: predicting the CTR using dynamic machine learning;

Cribs

• Probability;
• Machine learning;

Classification

• Article: does class balancing help to improve the results of the classifier;
• Quora: the advantages of various classification algorithms;
• Article: ROC analysis;
• Article: Inaccuracy Matrix - Terminology;

Linear regression

• Article: conditions for applying linear regression;
• duke.edu: all about linear regression;
• Data School: applying and evaluating linear regression results;
• ResearchGate: what if the dependent variable does not have a normal distribution;
• Wiki: multicollinearity;
• Article: Multicollinearity and Variance Inflation Factor (VIF);
• Article: regularization and selection of variables using the elastic neural network method;

Logistic regression

• Wiki: logistic regression;
• Article: geometric interpretation of logistic regression;
• FAQ: what is a pseudo R-square;

Model validation using resampling

• Wiki: resampling / resampling;
• Chioka: good cross-checking materials;
• Andrew Eun: preventing retraining during cross-checking;
• Gavin Cawley: the impact of retraining and systematic selection errors on the assessment of model performance;
• Andrew Moore: cross-check to identify and prevent retraining;
• Wiki: statistical bootstrap;
• Bootstrap: animations;
• Example: statistical bootstrap;

Deep learning

• List: guides, projects and deep learning communities;
• Deeplearning4j: deep learning resources;
• Stanford: interesting projects on deep learning and processing of natural language;
• Article: key concepts of deep learning;
• Article: Natural Language Processing with Deep Nets on Torch;
• Stanford: Deep Learning Guide;
• Quora: in-depth training FAQ;
• Google: a page on deep learning;
• Reddit: subreddit for deep learning;
• Reddit: another subreddit;

• Article: where to study deep learning;
• NVidia: deep learning concepts;
• Intro2deeplearning: deep learning and Python;
• Intro2deeplearning: great presentation;
• Oxford: video lectures 2015;
• Video: Deep Learning Summer School 2015;
• List: software for deep learning;
• Article: neural networks from the point of view of the programmer;
• Kdnuggets: top 5 works on deep learning;
• Video: Jeffrey Hinton on deep learning;
• Deeplearning: the best materials for deep learning;
• Deeplearning: all about machine learning;

• Deeplearning: machine learning software;
• Deeplearning4j: library guide;
• Article: a striking guide to deep learning;
• Article: The Basics of Deep Learning;
• Stanford: an article on deep learning;
• Deeplearning: deep learning guides;
• Article: neural machine translation using GPU (Part 1);
• Article: neural machine translation using GPU (Part 1);
• Article: neural machine translation using GPU (Part 1);
• Deep Speech: GPU speech recognition for deep neural network learning;

Deep Learning Framework

• FastML: Torch or Theano;
• Deeplearning4j: Dl4j, Torch7 or Theano;
• List: libraries for deep learning;
• Theano: Python library;
• Article: Introduction to Theano;
• Theano: a guide;
• Theano: another guide;
• Theano: apply logistic regression to classify numbers;
• Theano: multilayer perceptron;
• Theano: convolutional neural networks;

• Theano: recurrent neural network;
• Theano: LSTM networks for analyzing emotional coloration of statements;
• Theano: Boltzmann limited machine;
• Theano: deep web of trust;
• Theano: more guides;
• Torch: another library for machine learning;
• Manual: Torch machine learning;
• Article: Meet Torch;
• Repository: Torch tutorials;
• Repository: great stuff on Torch;

• Oxford: machine learning lectures using Torch;
• Torch: small review;
• Torch: tips and tricks;
• Torch: natural language processing using deep neural networks;
• Caffe: deep learning for solving computer vision problems with Caffe and cuDNN;
• TensorFlow: a machine learning library from Google;
• TensorFlow: examples for beginners;
• Repository: TensorFlow study materials;
• TensorFlow: benchmarks;

Direct propagation neural networks

• Lead: implementation of the neural network;
• Article: Accelerating a Neural Network with Theano and GPU;
• Article: basics of neural networks;
• Article: back propagation method;
• AI Junkie: neural network in C ++;
• Code Project: neural networks for beginners;
• Presentation: regression and classification algorithms;
• Article: acquaintance with neural networks;

Recurrent and LSTM networks

• Awesome-rnn: list of resources;
• Manual: recurrent neural network (Part 1);
• Manual: recurrent neural network (Part 2);
• Manual: recurrent neural network (Part 3);
• Article: natural language processing, recurrent networks and representations;
• Article: efficiency of recurrent neural networks;
• Deeplearning4j: introduction to recurrent neural networks;
• Deeplearning4j: introduction to LSTM networks;
• Article: application of recurrent neural networks;
• Articles: optimization of performance of recurrent networks;

• Example: simple recurrent neural network;
• Article: Clickbit header generation using recurrent neural networks;
• Presentation: using recurrent networks for text analysis;
• Article: using recurrent neural networks for machine translation;
• Keras: creating music using recurrent neural networks;
• Keras: using recurrent neural networks to generate dialogue;
• Article: introduction to LSTM networks;
• Article: LSTM networks;
• Deeplearning4j: LSTM networks for beginners;
• Article: LSTM network implementation from scratch;

• GitHub: a character model of a language and its implementation of char-rnn in Torch;
• GitHub: using LSTM networks for decoding EEG signals;
• Article: The use of LSTM networks for analyzing texts on Theano;
• Article: applying deep learning for image analysis;
• Google: computer responds to email using LSTM;
• Google: LSTM networks significantly improve voice search performance;
• Deeplearning: another article on the topic of voice search;
• NVidia: natural language processing with LSTM networks on Torch;
• Torch: image analysis using convolutional and LSTM networks;
• Comparison: LSTM or Managed Recurrent Modules (GRU);
• Wiki: recursive neural networks;
• Deeplearning4j: recursive tensor neural network (RNTN);
• Deeplearning4j: using word2vec, deep trust networks and RNTN for text analysis;

Limited Boltzmann machine

• Deeplearning4j: a guide for beginners on a limited Boltzmann machine;
• Deep Learning: another good guide;
• Article: introduction to limited Boltzmann machines;
• Jeffrey Hinton: a training manual for limited Boltzmann machines;
• GitHub: Limited Boltzmann machines on R;
• Deeplearning4j: guide to creating deep networks of trust;

Autocoders

• Andrew Eun: sparse autocoders;
• Deeplearning4j: guide to deep autocoders;
• Deep Learning: noise-canceling autocoders;
• Deep Learning: embedded noise-reducing auto-encoders;

Convolution networks

• Awesome Deep Vision: a list of resources for machine vision;
• Deeplearning4j: introduction to convolutional neural networks;
• Article: application of convolutional networks for natural language processing;
• Stanford: the use of convolutional networks for pattern recognition;
• Stanford: a JavaScript library for working with convolutional networks;
• Article: application of convolutional networks for face recognition;
• Article: creating a classifier of photos;
• Kaggle: interview with Jan Lekun;
• Article: Visualization of convolutional networks;

Natural language processing

• GitHub: list of speech and natural language processing resources;
• Article: Natural Language Processing with Deep Neural Networks on Torch;
• Guide: what is TF-IDF;
• Stanford: interesting projects related to natural language processing;
• Google: the basics of natural language processing;
• Manual: partial training for processing natural language in graphs;
• Model bag-of-words;
• Manual: text classification using the bag-of-words model;
• Thematic modeling;
• Latent placement of Dirichlet (LRD);

• Latent semantic analysis (LSA);
• Probabilistic latent semantic analysis (VLSA);
• Article: What is LJE;
• The article: Another good explanation of what is LRD;
• Article: Intuitive explanation of the LRD;
• Quora: what is the difference between LSA and LRD;
• Princeton: Dirichlet's latent placement;
• Quora: an intuitive explanation of the Dirichlet distribution;
• Article: Topical modeling is easy;

• Article: Real-time update of the LRD model;
• Article: Real-time update of the LRD model with Spark;
• Article: LRD on Scala (Part 1);
• Article: LRD on Scala (Part 2);
• Article: Segmentation of the Twitter event feed using thematic modeling;
• Article: building a thematic model of subscribers on Twitter;
• Google: word2vec;
• Wiki: bag-of-words model;
• Article: a thorough analysis of skip-gram models;
• Manual: skip-gram-modeling;

• Kaggle: vector representation of words;
• Article: how to work with word2vec;
• Deeplearning4j: word2vec algorithm;
• Quora: how word2vec works;
• Quora: about CBOW and skip-gram architectures in simple terms;
• Quora: what is the difference between BOW and CBOW;
• Quora: which is better for word2vec - CBOW or skip-gram;
• Wiki: Levenshtein distance;

• Article: classification of texts using the bag-of-words model;
• Article: language learning with natural language processing and reinforcement learning methods;
• Kaggle: vector representation of words and bag-of-words (Part 1);
• Kaggle: vector representation of words and bag-of-words (Part 2);
• Kaggle: vector representation of words and bag-of-words (Part 3);
• Manual: prediction of words in natural language processing;
• Article: more about skip-gram-modeling;

Computer vision

• Awesome Computer Vision: a list of materials on computer vision;
• Awesome Deep Vision: a list of resources for image recognition;

Support Vector Machine

• Quora: support vector machine in simple language;
• Guide: support vector machine;
• Guide: support vector machine;
• Presentation: support vector method;
• Article: introduction to the support vector method;

• Article: Comparison of Support Vector and Neural Networks;
• Article: optimization algorithms in the support vector machine;
• LIBSVM: library for classification by the support vector machine ;

• Quora: what are kernels in machine learning;
• Quora: Gaussian kernel in the support vector machine;
• Wiki: Platt scaling;
• Article: Calibrating Classifiers Using Platt Scaling;

Reinforcement training

• Awesome Reinforcement Learning: a list of resources for learning with reinforcement;
• Manual: reinforcement training (Part 1);
• Manual: reinforcement training (Part 2);

Decision trees

• Wiki: decision trees;
• FAQ: decision trees;
• Article: decisive forests and trees;
• Article: methods based on decision trees in R;
• Article: how decision trees work;
• Article: algorithms and their essence;
• Presentation: decision trees;

• Article: using surrogates to improve incomplete data sets;
• Article: decision trees;
• Wiki: pruning decision tree branches;
• Wiki: reverse process for cutting branches;
• Comparison: CART and CTREE algorithms;
• Comparison: algorithms CHAID and CART;

• Comparison: CART and CHAID algorithms;
• Article: another comparison of different algorithms;
• Wiki: recursive partitioning;
• Article: CART algorithm;
• CART: assessment of the importance of a variable;
• FAQ: recursive partitioning;
• Article: party package in R;
• Wiki: automatic chi-square interaction detector (CHAID);
• Article: introduction to CHAID;
• Manual: CHAID;

Mars

• Wiki: Multidimensional Adaptive Regression Splines (MARS);

Probabilistic decision trees

• Article: Bayesian learning in probabilistic decision trees;
• Article: probabilistic trees;

Random forest

• GitHub: a list of resources for a random forest;
• Kaggle: setting parameters of a random forest algorithm;
• Presentation: error out-of-bag;
• Article: Evaluating Random Forest Algorithms for Survival;
• FAQ: random forest;

Tree Boosting Algorithms

• Article: why you need a boosting;
• Wiki: boosting;
• Chen Tianzi: "growing" trees;
• Wiki: gradient boosting;
• Presentation: A generalized enhanced regression GBM model on R;
• FAQ: GBM;
• Kaggle: GBM or xgboost;
• Kaggle: setting parameters xgboost;
• Kaggle: xgboost or GBM;
• Review: xgboost;
• Wiki: AdaBoost;
• AdaBoost: working with a flat data set;
• Package: adaBag;
• AdaBoost: a guide;

Compositional training

• Wiki: compositional learning;
• Kaggle: compositional study guide;
• Article: introduction to compositional learning;
• Article: compositional learning;
• Composite models on R;
• Kaggle: prediction of soil properties in Africa;
• Comparison: boosting and bagging;

Stacking

• Article: Stacking, Blending and Multi-Layer Generalization;
• Article: multi-tiered generalization;
• Article: when to apply a multi-tiered generalization;
• Article: multi-tiered generalization;

Dimension Vapnik - Chervonenkisa

• Wiki: the dimension of Vapnik - Chervonenkis;
• Quora: an intuitive explanation of the Vapnik-Chervonenkis dimension;
• Video: what is the dimension of Vapnik - Chervonenkis;
• Article: acquaintance with the dimension of Vapnik - Chervonenkis;
• FAQ: the dimension of Vapnik - Chervonenkis;

Bayesian machine learning methods

• GitHub: an introduction to Bayesian machine learning techniques;
• Video: Should all machine learning methods be Bayesian;
• Guide: Bayesian optimization;
• Article: Bayesian inference and deep learning;
• Article: Bayesian statistics in simple words;
• GitHub: Kalman and Bayes filters in Python;
• Wiki: Markov chain;

Partial training

• Wiki: partial training;
• Manual: partial training;
• Hierarchical clustering (taxonomy);
• Video tutorial: partial training;
• Article: training with a teacher, without a teacher and partial training;
• Article: combining the possibilities of active and partial learning using the Gaussian random field model;
• Article: using the Gaussian random field model in partial learning;
• Article: improved partial learning algorithm;

Optimization

• Article: Optimization of the portfolio of assets on R using quadratic programming;
• Article: optimization algorithms in machine learning;
• Video: optimization algorithms in machine learning;
• Article: optimization algorithms in data analysis;
• Video: lectures on optimization;
• Article: optimization algorithms for the support vector machine;
• Article: the relationship between optimization problems and machine learning;

Additionally

• GitHub: a collection of guides for using R in data science.