Algorithms.md

ALGORITHMS and Data Structures : Data Mining, Clustering, Machine Learning, Neural, NLP, ...

• COMPUTATIONAL PROBLEMS
  • NP-complete
    • SAT
• DATA STRUCTURES
• HMM
• RESOURCES

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COMPUTATIONAL PROBLEMS

NP-complete

SAT

• sat-solver :: SAT solver for use in Enstaller, based on the MiniSat implementation.

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DATA STRUCTURES

Arrays / Hash tables / Matrix / Functional / Trees data structures

• CGT :: Computation Graph Toolkit (CGT) is a library for evaluation and differentiation of functions of multidimensional arrays. The author's announcement on his blog.
• Datarray :: Prototyping numpy arrays with named axes for data management. Docs are available at: http://fperez.github.com/datarray-doc
• distarray :: DistArray provides general multidimensional NumPy-like distributed arrays to Python and intends to bring the strengths of NumPy to data-parallel high-performance computing. DistArray has a similar API to NumPy. Documentation.
• keras :: Theano-based Deep Learning library.
• LA :: Label the rows, columns, any dimension, of your NumPy arrays. The main class of the la package is a labeled array, larry. A larry consists of data and labels and data is stored as a NumPy array with labels as a list of lists (one list per dimension).
• Lasagne :: A Lightweight library to build and train neural networks in Theano. Documentation.
• netcdf4-python :: A python/numpy interface to the netCDF C library. http://unidata.github.io/netcdf4-python
• propagator.py :: A propagator network in Python, inspired by Radul & Sussman's The Art of the Propagator.
• PyGraphistry :: A library to extract, transform, and visually explore big graphs.
• pymf :: Python Matrix Factorization Module. Source on Google
• PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.
• python-graph :: A library for working with graphs in Python.
• sparray :: Sparse n-dimensional arrays in Python.
• Theano :: A Python library for working with mathematical expressions involving multi-dimensional arrays efficiently. Resources
  • Official Documentation.
  • IPython Theano Tutorials
  • A brief IPython notebook-based tutorial on basic Theano concepts, including a toy multi-layer perceptron example.
• Xray :: Extended arrays for working with scientific datasets in Python. Documentation

scikit

• scikits-bootstrap :: Bootstrap Scikit for bootstrap confidence interval estimation.
• scikit-learn :: Machine Learning in Python. Resources
  • Riding on Large Data with Scikit-learn.
  • odscon-sf-2015 :: Slides and Notebooks for Open Data Science Conference - ODSCON San Francisco 2015.
  • scipy_2015_sklearn_tutorial :: Scikit-Learn tutorial material for Scipy 2015.
  • Scikit-learn Tutorial at EuroPython 2014
  • Using scikit-learn Pipelines and FeatureUnions
  • parallel_ml_tutorial :: Tutorial on parallel Machine Learning with scikit-learn and IPython.
  • postlearn :: Common post-estimation tasks for scikit-learn.
  • sklearn_pycon2015 :: Materials for @jakevdp's Pycon 2015 scikit-learn tutorial.
• scikits-sparse → Sparse matrix tools extending scipy.sparse, but with incompatible licenses. Resources
  • Gradient Boosted Regression Trees in scikit-learn
  • PyData-2014 Talk → Know Thy Neighbor: Scikit-Learn and kNN Algorithm Tutorial and PyCon 2014 Talk
  • K-means Clustering with Scikit-Learn
  • @ageitgey gives an introduction to the basics of Machine Learning
  • The Computational Complexity of Machine Learning
  • Expensive lessons in Python performance tuning
  • A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
• sklearn-pandas :: This module provides a bridge between Scikit-Learn's machine learning methods and pandas-style Data Frames.
• sklearn-theano :: Scikit-learn compatible tools using Theano.
  • Paper: Fast Bird Part Localization for Fine-Grained Categorization, Yaser Souri, Shohreh Kasaei, Sharif University of Technology.

Resources

• Problem Solving with Algorithms and Data Structures by Brad Miller and David Ranum, Luther College.

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HMM

• YAHMM :: Yet Another Hidden Markov Model repository.

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RESOURCES

• bci-challenge-ner-2015 :: Code and documentation for the winning solution at the BCI Challenge @ NER 2015 : https://www.kaggle.com/c/inria-bci-challenge
• GraphLab_Practice