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.

##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
  • Convolutional 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

• 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

• Resampling Explained
• Partioning data set in R
• Implementing hold-out Validaion in R, 2

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• 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

• 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

- [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)
    - [Deep Learning Implementation Tutorials - Keras and Lasagne](https://github.com/vict0rsch/deep_learning/)

- [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](https://devblogs.nvidia.com/parallelforall/deep-learning-computer-vision-caffe-cudnn/)

- TensorFlow
    - [Website](http://tensorflow.org/)
    - [TensorFlow Examples for Beginners](https://github.com/aymericdamien/TensorFlow-Examples)
    - [Stanford Tensorflow for Deep Learning Research Course](https://web.stanford.edu/class/cs20si/syllabus.html)
        - [GitHub Repo](https://github.com/chiphuyen/tf-stanford-tutorials)
    - [Simplified Scikit-learn 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/convnet-benchmarks/issues/66)
    - [Awesome TensorFlow List](https://github.com/jtoy/awesome-tensorflow)
    - [TensorFlow Book](https://github.com/BinRoot/TensorFlow-Book)

• 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

• 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

• 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