{"id":15685922,"url":"https://github.com/gerdm/misc","last_synced_at":"2025-10-04T16:47:32.873Z","repository":{"id":88732833,"uuid":"289472614","full_name":"gerdm/misc","owner":"gerdm","description":"Collection of code without a particular purpose.","archived":false,"fork":false,"pushed_at":"2025-06-26T05:12:47.000Z","size":81922,"stargazers_count":10,"open_issues_count":0,"forks_count":5,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-06-26T06:24:25.142Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Gerardo's collecion of notebooks without a particular purpose\n\n```\n.\n├── 2020-08\n│   ├── dtrees-svm.ipynb\n│   ├── index-model-ml.ipynb\n│   ├── magician.ipynb\n│   ├── probit-logistic.ipynb\n│   └── small-pandas.ipynb\n├── 2020-09\n│   ├── bishop-analysis.ipynb\n│   ├── nn-selection.ipynb\n│   ├── pknn.ipynb\n│   ├── q-learning.ipynb\n│   └── students-t.ipynb\n├── 2020-10\n│   ├── julia-101.ipynb\n│   └── neurips-analysis.ipynb\n├── 2020-12\n│   ├── julia-design-patterns.ipynb\n│   ├── optimisers.ipynb\n│   └── student-t-distribution.ipynb\n├── 2021-01\n│   └── deep-kalman-filters.ipynb\n├── 2021-04\n│   ├── 4-at-stable.pdf\n│   ├── chaos_process.pdf\n│   ├── complex-systems.ipynb\n│   ├── duffing-oscilator.gif\n│   ├── log-1.pdf\n│   ├── logistic-iterations.gif\n│   ├── map1.pdf\n│   ├── map2.pdf\n│   ├── map3.pdf\n│   ├── map4.pdf\n│   ├── map5.pdf\n│   ├── random_process.pdf\n│   ├── tmp.gif\n│   └── trajectory-histogram.pdf\n├── 2021-05\n│   ├── gibbs-gaussian.ipynb\n│   ├── kalman-smoothing.ipynb\n│   └── mcmc_gmm_demo.ipynb\n├── 2021-06\n│   ├── __pycache__\n│   │   ├── linear_dynamical_systems_lib.cpython-38.pyc\n│   │   ├── nlds_lib.cpython-38.pyc\n│   │   ├── pyprobml_utils.cpython-38.pyc\n│   │   ├── variational_mixture_gaussians.cpython-38.pyc\n│   │   └── variational_mixture_gaussians.cpython-39.pyc\n│   ├── dishonest_casino.ipynb\n│   ├── extended-kalman-filter.ipynb\n│   ├── mix_gauss_vb.ipynb\n│   ├── nlds_lib.py\n│   ├── parallel_kalman_filter.ipynb\n│   ├── pyprobml_utils.py\n│   ├── ukf-ekf-mlp.ipynb\n│   ├── unscented-kalman-filter.ipynb\n│   └── variational_mixture_gaussians.py\n├── 2021-07\n│   ├── __pycache__\n│   │   └── nlds_lib.cpython-38.pyc\n│   ├── bayesian-cart.ipynb\n│   ├── example1_graph.png\n│   ├── gpr_demo_marlik.ipynb\n│   ├── knn_tpu.ipynb\n│   ├── linreg_sel.ipynb\n│   ├── nlds_lib.py\n│   ├── particle-filtering.ipynb\n│   └── uni_gauss_vb.ipynb\n├── 2021-08\n│   ├── adf-ekf-logistic-regression.ipynb\n│   ├── bootstrap-filter-maneuver.ipynb\n│   ├── gsoc-analysis.ipynb\n│   ├── gsoc-presentation.ipynb\n│   ├── imagenette_clip_logreg_demo.ipynb\n│   └── qmul-repo-analysis.ipynb\n├── 2021-09\n│   └── thompson-sampling-bandits.ipynb\n├── 2021-11\n│   └── importance-sampling.ipynb\n├── 2021-12\n│   ├── measurable-function-approx.ipynb\n│   └── tpu-init.sh\n├── 2022-01\n│   ├── bayesian-logreg-refactor.ipynb\n│   ├── einsums-in-the-wild.ipynb\n│   ├── non-markovian-gaussian-sequence-model.ipynb\n│   └── year-in-review.ipynb\n└── README.md\n\n16 directories, 71 files\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fmisc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgerdm%2Fmisc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fmisc/lists"}