{"id":13510770,"url":"https://github.com/rougier/ML-Recipes","last_synced_at":"2025-03-30T17:31:20.915Z","repository":{"id":45658258,"uuid":"171512896","full_name":"rougier/ML-Recipes","owner":"rougier","description":"A collection of stand-alone Python machine learning recipes","archived":false,"fork":false,"pushed_at":"2021-06-30T15:03:23.000Z","size":5013,"stargazers_count":662,"open_issues_count":0,"forks_count":66,"subscribers_count":15,"default_branch":"master","last_synced_at":"2025-03-25T02:06:08.330Z","etag":null,"topics":["algorithm","awesome","machine-learning","neural-network","python","recipes","reinforcement-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rougier.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-02-19T16:52:03.000Z","updated_at":"2025-03-17T14:13:12.000Z","dependencies_parsed_at":"2022-07-31T01:17:55.359Z","dependency_job_id":null,"html_url":"https://github.com/rougier/ML-Recipes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rougier%2FML-Recipes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rougier%2FML-Recipes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rougier%2FML-Recipes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rougier%2FML-Recipes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rougier","download_url":"https://codeload.github.com/rougier/ML-Recipes/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246355383,"owners_count":20763990,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithm","awesome","machine-learning","neural-network","python","recipes","reinforcement-learning"],"created_at":"2024-08-01T02:01:53.513Z","updated_at":"2025-03-30T17:31:20.878Z","avatar_url":"https://github.com/rougier.png","language":"Python","readme":"\n\u003ca href=\"https://xkcd.com/1838/\"\u003e\u003cimg src=\"xkcd.png\" align=\"right\"/\u003e\u003c/a\u003e\n\n# Machine Learning Recipes\n\nThis is a collection of stand-alone Python examples of machine learning\nalgorithms. Run a specific recipe to see usage and result. Feel free to\ncontribute an example (recipe should be reasonably small, including usage).\n\n### [Multi-armed bandit (MAB)](https://en.wikipedia.org/wiki/Multi-armed_bandit)\n\n* **Epsilon greedy** ([recipes/MAB/greedy.py](recipes/MAB/greedy.py))\n  \u003e Sutton, Richard S., Barto, Andrew G. \"Reinforcement Learning: An\n  \u003e Introduction\", MIT Press, Cambridge, MA (1998).\n\n* **Softmax** ([recipes/MAB/softmax.py](recipes/MAB/softmax.py))\n  \u003e Luce, R. Duncan. (1963). \"Detection and recognition\". In Luce, R. Duncan,\n  \u003e Bush, Robert. R. \u0026 Galanter, Eugene (Eds.), \"Handbook of mathematical\n  \u003e psychology\" (Vol. 1), New York: Wiley.\n\n* **Thompson sampling** ([recipes/MAB/thompson.py](recipes/MAB/thompson.py))\n  \u003e Thompson, William R. On the likelihood that one unknown probability exceeds\n  \u003e another in view of the evidence of two samples. Biometrika,\n  \u003e 25(3–4):285–294, 1933.  DOI: [10.2307/2332286](http://doi.org/10.2307/2332286)\n\n* **Upper Confidence Bound** ([recipes/MAB/ucb.py](recipes/MAB/ucb.py))\n  \u003e Lai, T.L and Robbins, Herbert, \"Asymptotically efficient adaptive\n  \u003e allocation rules\", Advances in Applied Mathematics 6:1, (1985) DOI:\n  \u003e [10.1016/0196-8858(85)90002-8](http://doi.org/10.1016/0196-8858(85)90002-8)\n\n\n### [Artificial Neural Network (ANN)](https://en.wikipedia.org/wiki/Artificial_neural_network)\n\n* **Adaptive Resonance Theory** ([recipes/ANN/art.py](recipes/ANN/art.py))\n\n  \u003e Grossberg, Stephen (1987). Competitive learning: From interactive\n  \u003e activation to adaptive resonance, Cognitive Science, 11, 23-63.\n\n* **Echo State Network** ([recipes/ANN/esn.py](recipes/ANN/esn.py))\n\n  \u003e Jaeger, Herbert (2001) The \"echo state\" approach to analysing and training\n  \u003e recurrent neural networks. GMD Report 148, GMD - German National Research\n  \u003e Institute for Computer Science.\n\n* **Simple Recurrent Network** ([recipes/ANN/srn.py](recipes/ANN/srn.py))\n\n  \u003e Elman, Jeffrey L. (1990). Finding structure in time. Cognitive Science,\n  \u003e 14:179–211.\n\n* **Long Short Term Memory** ([nicodjimenez/lstm](https://github.com/nicodjimenez/lstm))\n\n  \u003e Hochreiter, Sepp and Schmidhuber, Jürgen (1997) Long Short-Term Memory,\n  \u003e Neural Computation Vol. 9, 1735-1780\n\n* **Multi-Layer Perceptron** ([recipes/ANN/mlp.py](recipes/ANN/mlp.py))\n\n  \u003e Rumelhart, David E., Hinton, Geoffrey E. and Williams, Ronald J. \"Learning\n  \u003e Internal Representations by Error Propagation\". Rumelhart, David E.,\n  \u003e McClelland, James L., and the PDP research group. (editors), Parallel\n  \u003e distributed processing: Explorations in the microstructure of cognition,\n  \u003e Volume 1: Foundation. MIT Press, 1986.\n\n* **Perceptron** ([recipes/ANN/perceptron.py](recipes/ANN/perceptron.py))\n\n  \u003e Rosenblatt, Frank (1958), \"The Perceptron: A Probabilistic Model for\n  \u003e Information Storage and Organization in the Brain\", Cornell Aeronautical\n  \u003e Laboratory, Psychological Review, v65, No. 6,\n  \u003e pp. 386–408. DOI:[10.1037/h0042519](http://doi.org/10.1037/h0042519)\n\n* **Kernel perceptron** ([recipes/ANN/kernel-perceptron.py](recipes/ANN/kernel-perceptron.py))\n\n  \u003e Aizerman, M. A., Braverman, E. A. and Rozonoer, L.. \" Theoretical\n  \u003e foundations of the potential function method in pattern \n  \u003e recognition learning..\" Paper presented at the meeting of the\n  \u003e Automation and Remote Control,, 1964.\n\n* **Voted Perceptron** ([recipes/ANN/voted-perceptron.py](recipes/ANN/voted-perceptron.py))\n\n  \u003e Y. Freund, R. E. Schapire. \"Large margin classification using\n  \u003e the perceptron algorithm\". In: 11th Annual Conference on\n  \u003e Computational Learning Theory, New York, NY, 209-217, 1998.\n  \u003e DOI:[10.1023/A:1007662407062](http://doi.org/10.1023/A:1007662407062)\n\n* **Self Organizing Map** ([recipes/ANN/som.py](recipes/ANN/som.py))\n\n  \u003e Kohonen, Teuvo. Self-Organization and Associative Memory. Springer, Berlin,\n  \u003e 1984.\n\n\n### [Markov Decision Process (MDP)](https://en.wikipedia.org/wiki/Markov_decision_process)\n\n* **Value Iteration**  ([recipes/MDP/value-iteration.py](recipes/MDP/value-iteration.py))\n\n  \u003e Bellman, Richard (1957). \"A Markovian Decision Process\". Journal of\n  \u003e Mathematics and Mechanics. 6.\n\n### [Dimensionality Reduction (DR)](https://en.wikipedia.org/wiki/Dimensionality_reduction)\n\n* **Principal Component Analysis** ([recipes/DR/pca.py](recipes/DR/pca.py))\n\n  \u003e Pearson, K. (1901). \"On Lines and Planes of Closest Fit to Systems\n  \u003e of Points in Space\". Philosophical Magazine. 2 (11): 559–572.\n  \u003e DOI:[10.1080/14786440109462720](http://doi.org/10.1080/14786440109462720)\n\n* **Eigenface** ([recipes/DR/eigenface.py](recipes/DR/eigenface.py))\n\n  \u003e M. Turk \u0026 A. Pentland (1991) Eigenfaces for Recognition.\n  \u003e Journal of cognitive neuroscience, 3(1): 71-86.\n  \u003e DOI:[10.1162/jocn.1991.3.1.71](https://doi.org/10.1162/jocn.1991.3.1.71)\n\n* **Classical Multidimensional scaling** ([recipes/DR/classical_mds.py](recipes/DR/classical_mds.py))\n\n  \u003e W.S. Torgerson (1952) Multidimensional scaling: I. Theory and method\n  \u003e Psychometrika, 17: 401-419\n  \u003e DOI:[10.1007/BF02288916](https://doi.org/10.1007/BF02288916)\n\n","funding_links":[],"categories":["Python","awesome"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frougier%2FML-Recipes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frougier%2FML-Recipes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frougier%2FML-Recipes/lists"}