Machine 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.

##Table of Contents

Miscellaneous
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
- Convolution 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

##Logistic Regression - [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression) - [Geometric Intuition of Logistic Regression](http://florianhartl.com/logistic-regression-geometric-intuition.html) - [Obtaining predicted categories (choosing threshold)](http://stats.stackexchange.com/questions/25389/obtaining-predicted-values-y-1-or-0-from-a-logistic-regression-model-fit) - [Residuals in logistic regression](http://stats.stackexchange.com/questions/1432/what-do-the-residuals-in-a-logistic-regression-mean) - [Difference between logit and probit models](http://stats.stackexchange.com/questions/20523/difference-between-logit-and-probit-models#30909), [Logistic Regression Wiki](https://en.wikipedia.org/wiki/Logistic_regression), [Probit Model Wiki](https://en.wikipedia.org/wiki/Probit_model) - [Pseudo R2 for Logistic Regression](http://stats.stackexchange.com/questions/3559/which-pseudo-r2-measure-is-the-one-to-report-for-logistic-regression-cox-s), [How to calculate](http://stats.stackexchange.com/questions/8511/how-to-calculate-pseudo-r2-from-rs-logistic-regression), [Other Details](http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm) ##Model Validation using Resampling

- [Cross Validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) - [Training with Full dataset after CV?](http://stats.stackexchange.com/questions/11602/training-with-the-full-dataset-after-cross-validation) - [Which CV method is best?](http://stats.stackexchange.com/questions/103459/how-do-i-know-which-method-of-cross-validation-is-best) - [Variance Estimates in k-fold CV](http://stats.stackexchange.com/questions/31190/variance-estimates-in-k-fold-cross-validation) - [Is CV a subsitute for Validation Set?](http://stats.stackexchange.com/questions/18856/is-cross-validation-a-proper-substitute-for-validation-set) - [Choice of k in k-fold CV](http://stats.stackexchange.com/questions/27730/choice-of-k-in-k-fold-cross-validation) - [CV for ensemble learning](http://stats.stackexchange.com/questions/102631/k-fold-cross-validation-of-ensemble-learning) - [k-fold CV in R](http://stackoverflow.com/questions/22909197/creating-folds-for-k-fold-cv-in-r-using-caret) - [Good Resources](http://www.chioka.in/tag/cross-validation/) - Overfitting and Cross Validation - [Preventing Overfitting the Cross Validation Data | Andrew Ng](http://ai.stanford.edu/~ang/papers/cv-final.pdf) - [Over-fitting 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/how-does-cross-validation-overcome-the-overfitting-problem)

<a name="boot" />

Bootstrapping

Neural Machine Translation
- 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](http://fastml.com/torch-vs-theano/) - [dl4j vs. torch7 vs. theano](http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html) - [Deep Learning Libraries by Language](http://www.teglor.com/b/deep-learning-libraries-language-cm569/)

- [Theano](https://en.wikipedia.org/wiki/Theano_(software))
    - [Website](http://deeplearning.net/software/theano/) 
    - [Theano Introduction](http://www.wildml.com/2015/09/speeding-up-your-neural-network-with-theano-and-the-gpu/)
    - [Theano Tutorial](http://outlace.com/Beginner-Tutorial-Theano/)
    - [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/lisa-lab/DeepLearningTutorials)
    
- [Torch](http://torch.ch/)
    - [Torch ML Tutorial](http://code.madbits.com/wiki/doku.php), [Code](https://github.com/torch/tutorials)
    - [Intro to Torch](http://ml.informatik.uni-freiburg.de/_media/teaching/ws1415/presentation_dl_lect3.pdf)
    - [Learning Torch GitHub Repo](https://github.com/chetannaik/learning_torch)
    - [Awesome-Torch (Repository on GitHub)](https://github.com/carpedm20/awesome-torch)
    - [Machine Learning using Torch Oxford Univ](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/), [Code](https://github.com/oxford-cs-ml-2015)
    - [Torch Internals Overview](https://apaszke.github.io/torch-internals.html)     
    - [Torch Cheatsheet](https://github.com/torch/torch7/wiki/Cheatsheet)
    - [Understanding Natural Language with Deep Neural Networks Using Torch](http://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/)
    
- Caffe
    - [Deep Learning for Computer Vision with Caffe and cuDNN](http://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/)

- TensorFlow 
    - [Website](http://tensorflow.org/)
    - [Learning TensorFlow GitHub Repo](https://github.com/chetannaik/learning_tensorflow)
    - [Benchmark TensorFlow GitHub](https://github.com/soumith/convnet-benchmarks/issues/66)

xgboost
AdaBoost

stargt/Machine-Learning-Tutorials

Machine Learning Tutorials