This is a collection of stand-alone Python examples of machine learning algorithms. Run a specific recipe to see usage and result. Feel free to contribute an example (recipe should be reasonably small, including usage).

• Epsilon greedy (recipes/MAB/greedy.py)

  Sutton, Richard S., Barto, Andrew G. "Reinforcement Learning: An Introduction", MIT Press, Cambridge, MA (1998).

• Softmax (recipes/MAB/softmax.py)

  Luce, R. Duncan. (1963). "Detection and recognition". In Luce, R. Duncan, Bush, Robert. R. & Galanter, Eugene (Eds.), "Handbook of mathematical psychology" (Vol. 1), New York: Wiley.

• Thompson sampling (recipes/MAB/thompson.py)

  Thompson, William R. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3–4):285–294, 1933. DOI: 10.2307/2332286

• Upper Confidence Bound (recipes/MAB/ucb.py)

  Lai, T.L and Robbins, Herbert, "Asymptotically efficient adaptive allocation rules", Advances in Applied Mathematics 6:1, (1985) DOI: 10.1016/0196-8858(85)90002-8

• Adaptive Resonance Theory (recipes/ANN/art.py)

  Grossberg, Stephen (1987). Competitive learning: From interactive activation to adaptive resonance, Cognitive Science, 11, 23-63.

• Echo State Network (recipes/ANN/esn.py)

  Jaeger, Herbert (2001) The "echo state" approach to analysing and training recurrent neural networks. GMD Report 148, GMD - German National Research Institute for Computer Science.

• Simple Recurrent Network (recipes/ANN/srn.py)

  Elman, Jeffrey L. (1990). Finding structure in time. Cognitive Science, 14:179–211.

• Long Short Term Memory (nicodjimenez/lstm)

  Hochreiter, Sepp and Schmidhuber, Jürgen (1997) Long Short-Term Memory, Neural Computation Vol. 9, 1735-1780

• Multi-Layer Perceptron (recipes/ANN/mlp.py)

  Rumelhart, David E., Hinton, Geoffrey E. and Williams, Ronald J. "Learning Internal Representations by Error Propagation". Rumelhart, David E., McClelland, James L., and the PDP research group. (editors), Parallel distributed processing: Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press, 1986.

• Perceptron (recipes/ANN/perceptron.py)

  Rosenblatt, Frank (1958), "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain", Cornell Aeronautical Laboratory, Psychological Review, v65, No. 6, pp. 386–408. DOI:10.1037/h0042519

• Kernel perceptron (recipes/ANN/kernel-perceptron.py)

  Aizerman, M. A., Braverman, E. A. and Rozonoer, L.. " Theoretical foundations of the potential function method in pattern recognition learning.." Paper presented at the meeting of the Automation and Remote Control,, 1964.

• Voted Perceptron (recipes/ANN/voted-perceptron.py)

  Y. Freund, R. E. Schapire. "Large margin classification using the perceptron algorithm". In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998. DOI:10.1023/A:1007662407062

• Self Organizing Map (recipes/ANN/som.py)

  Kohonen, Teuvo. Self-Organization and Associative Memory. Springer, Berlin, 1984.

• Value Iteration (recipes/MDP/value-iteration.py)

  Bellman, Richard (1957). "A Markovian Decision Process". Journal of Mathematics and Mechanics. 6.

• Principal Component Analysis (recipes/DR/pca.py)

  Pearson, K. (1901). "On Lines and Planes of Closest Fit to Systems of Points in Space". Philosophical Magazine. 2 (11): 559–572. DOI:10.1080/14786440109462720

• Eigenface (recipes/DR/eigenface.py)

  M. Turk & A. Pentland (1991) Eigenfaces for Recognition. Journal of cognitive neuroscience, 3(1): 71-86. DOI:10.1162/jocn.1991.3.1.71

• Classical Multidimensional scaling (recipes/DR/classical_mds.py)

  W.S. Torgerson (1952) Multidimensional scaling: I. Theory and method Psychometrika, 17: 401-419 DOI:10.1007/BF02288916