machine learning and deep learning tutorials, articles and other resources
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This repository contains a topicwise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.
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Curated list of R tutorials for Data Science, NLP and Machine Learning.
Curated list of Python tutorials for Data Science, NLP and Machine Learning.
Indepth introduction to machine learning in 15 hours of expert videos
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
Machine Learning algorithms that you should always have a strong understanding of
Difference between Linearly Independent, Orthogonal, and Uncorrelated Variables
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?
Programming Community Curated Resources for learning Artificial Intelligence
MIT 6.034 Artificial Intelligence Lecture Videos, Complete Course
Stat Trek Website  A dedicated website to teach yourselves Statistics
Learn Statistics Using Python  Learn Statistics using an applicationcentric programming approach
Statistics for Hackers  Slides  @jakevdp  Slides by Jake VanderPlas
Online Statistics Book  An Interactive Multimedia Course for Studying Statistics
Tutorials
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 opensource library usage
Multicollinearity and VIF
Difference between logit and probit models, Logistic Regression Wiki, Probit Model Wiki
Pseudo R2 for Logistic Regression, How to calculate, Other Details
Overfitting and Cross Validation
 [Preventing Overfitting the Cross Validation Data  Andrew Ng](http://ai.stanford.edu/%7Eang/papers/cvfinal.pdf)
 [Overfitting in Model Selection and Subsequent Selection Bias in Performance Evaluation](http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf)
 [CV for detecting and preventing Overfitting](http://www.autonlab.org/tutorials/overfit10.pdf)
 [How does CV overcome the Overfitting Problem](http://stats.stackexchange.com/questions/9053/howdoescrossvalidationovercometheoverfittingproblem)
A curated list of awesome Deep Learning tutorials, projects and communities
Interesting Deep Learning and NLP Projects (Stanford), Website
Understanding Natural Language with Deep Neural Networks Using Torch
Introduction to Deep Learning Using Python (GitHub), Good Introduction Slides
Video Lectures Oxford 2015, Video Lectures Summer School Montreal
Neural Machine Translation
Deep Learning Frameworks
 [Website](http://deeplearning.net/software/theano/)
 [Theano Introduction](http://www.wildml.com/2015/09/speedingupyourneuralnetworkwiththeanoandthegpu/)
 [Theano Tutorial](http://outlace.com/BeginnerTutorialTheano/)
 [Good Theano Tutorial](http://deeplearning.net/software/theano/tutorial/)
 [Logistic Regression using Theano for classifying digits](http://deeplearning.net/tutorial/logreg.html#logreg)
 [MLP using Theano](http://deeplearning.net/tutorial/mlp.html#mlp)
 [CNN using Theano](http://deeplearning.net/tutorial/lenet.html#lenet)
 [RNNs using Theano](http://deeplearning.net/tutorial/rnnslu.html#rnnslu)
 [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm)
 [RBM using Theano](http://deeplearning.net/tutorial/rbm.html#rbm)
 [DBNs using Theano](http://deeplearning.net/tutorial/DBN.html#dbn)
 [All Codes](https://github.com/lisalab/DeepLearningTutorials)
 [Deep Learning Implementation Tutorials  Keras and Lasagne](https://github.com/vict0rsch/deep_learning/)
Caffe
 [Deep Learning for Computer Vision with Caffe and cuDNN](https://devblogs.nvidia.com/parallelforall/deeplearningcomputervisioncaffecudnn/)
TensorFlow
Stanford Tensorflow for Deep Learning Research Course
 [GitHub Repo](https://github.com/chiphuyen/tfstanfordtutorials)
 [Simplified Scikitlearn Style Interface to TensorFlow](https://github.com/tensorflow/skflow)
 [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow)
 [Benchmark TensorFlow GitHub](https://github.com/soumith/convnetbenchmarks/issues/66)
 [Awesome TensorFlow List](https://github.com/jtoy/awesometensorflow)
 [TensorFlow Book](https://github.com/BinRoot/TensorFlowBook)

Android TensorFlow Machine Learning Example
 [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMachineLearningExample)

Creating Custom Model For Android Using TensorFlow
 [GitHub Repo](https://github.com/MindorksOpenSource/AndroidTensorFlowMNISTExample)
Feed Forward Networks
Recurrent and LSTM Networks
Long Short Term Memory (LSTM)
 [Understanding LSTM Networks](http://colah.github.io/posts/201508UnderstandingLSTMs/)
 [LSTM explained](https://apaszke.github.io/lstmexplained.html)
 [Beginner’s Guide to LSTM](http://deeplearning4j.org/lstm.html)
 [Implementing LSTM from scratch](http://www.wildml.com/2015/10/recurrentneuralnetworktutorialpart4implementingagrulstmrnnwithpythonandtheano/), [Python/Theano code](https://github.com/dennybritz/rnntutorialgrulstm)
 [Torch Code for characterlevel language models using LSTM](https://github.com/karpathy/charrnn)
 [LSTM for Kaggle EEG Detection competition (Torch Code)](https://github.com/apaszke/kagglegraspandlift)
 [LSTM for Sentiment Analysis in Theano](http://deeplearning.net/tutorial/lstm.html#lstm)
 [Deep Learning for Visual Q&A  LSTM  CNN](http://avisingh599.github.io/deeplearning/visualqa/), [Code](https://github.com/avisingh599/visualqa)
 [Computer Responds to email using LSTM  Google](http://googleresearch.blogspot.in/2015/11/computerrespondtothisemail.html)
 [LSTM dramatically improves Google Voice Search](http://googleresearch.blogspot.ch/2015/09/googlevoicesearchfasterandmore.html), [Another Article](http://deeplearning.net/2015/09/30/longshorttermmemorydramaticallyimprovesgooglevoiceetcnowavailabletoabillionusers/)
 [Understanding Natural Language with LSTM Using Torch](http://devblogs.nvidia.com/parallelforall/understandingnaturallanguagedeepneuralnetworksusingtorch/)
 [Torch code for Visual Question Answering using a CNN+LSTM model](https://github.com/abhshkdz/neuralvqa)
 [LSTM for Human Activity Recognition](https://github.com/guillaumechevalier/LSTMHumanActivityRecognition/)
Gated Recurrent Units (GRU)
Time series forecasting with SequencetoSequence (seq2seq) rnn models
Restricted Boltzmann Machine
Autoencoders: Unsupervised (applies BackProp after setting target = input)
Convolutional Neural Networks
Network Representation Learning
A curated list of speech and natural language processing resources
Understanding Natural Language with Deep Neural Networks Using Torch
Topic Modeling
word2vec
Text Clustering
Text Classification
Named Entity Recognitation
Comparisons
Software
Kernels
Probabilities post SVM
What is entropy and information gain in the context of building decision trees?
How do decision tree learning algorithms deal with missing values?
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
CTREE
CHAID
MARS
Probabilistic Decision Trees
Evaluating Random Forests for Survival Analysis Using Prediction Error Curve
Why doesn't Random Forest handle missing values in predictors?
Gradient Boosting Machine
xgboost
AdaBoost
CatBoost
Mean Variance Portfolio Optimization with R and Quadratic Programming
Hyperopt tutorial for Optimizing Neural Networks’ Hyperparameters