{"id":26884361,"url":"https://github.com/kataglyphis/machinelearningalgorithms","last_synced_at":"2025-03-31T17:58:55.867Z","repository":{"id":41398668,"uuid":"420132150","full_name":"Kataglyphis/MachineLearningAlgorithms","owner":"Kataglyphis","description":"Basic Machine Learning Algorithms ","archived":false,"fork":false,"pushed_at":"2024-01-31T13:55:19.000Z","size":44380,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-28T22:12:31.867Z","etag":null,"topics":["cuda","machine-learning","python","tensorflow"],"latest_commit_sha":null,"homepage":"https://jotrockenmitlocken.de/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Kataglyphis.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":"2021-10-22T14:34:48.000Z","updated_at":"2022-06-24T12:37:28.000Z","dependencies_parsed_at":"2022-09-01T20:22:17.741Z","dependency_job_id":null,"html_url":"https://github.com/Kataglyphis/MachineLearningAlgorithms","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/Kataglyphis%2FMachineLearningAlgorithms","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kataglyphis%2FMachineLearningAlgorithms/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kataglyphis%2FMachineLearningAlgorithms/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Kataglyphis%2FMachineLearningAlgorithms/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Kataglyphis","download_url":"https://codeload.github.com/Kataglyphis/MachineLearningAlgorithms/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246515087,"owners_count":20790021,"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":["cuda","machine-learning","python","tensorflow"],"created_at":"2025-03-31T17:58:55.064Z","updated_at":"2025-03-31T17:58:55.832Z","avatar_url":"https://github.com/Kataglyphis.png","language":"Python","funding_links":["https://www.paypal.com/donate/?hosted_button_id=BX9AVVES2P9LN","https://paypal.me/JonasHeinle?locale.x=de_DE"],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e\r\n  \u003cbr\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/logo.png\" alt=\"MLAlgo\" width=\"150\"\u003e\u003c/a\u003e\r\n  \u003cbr\u003e\r\n    MLPlayground\r\n  \u003cbr\u003e\r\n  \u003cbr\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/Python_logo.png\" alt=\"python logo\" width=\"50\"\u003e\u003c/a\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/Tensorflow_logo.svg.png\" alt=\"tensorflow icon\" width=\"50\"\u003e\u003c/a\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/cupy_icon.png\" alt=\"cupy icon\" width=\"50\"\u003e\u003c/a\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/anaconda_icon.png\" alt=\"anaconda icon\" width=\"50\"\u003e\u003c/a\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/pytorch.svg.png\" alt=\"pytorch symbol\" width=\"50\"\u003e\u003c/a\u003e\r\n  \u003cbr\u003e\r\n\u003c/h1\u003e\r\n  \r\n[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Kataglyphis/MachineLearningAlgorithms/Python%20application%20on%20ubuntu?label=Ubuntu%20build\u0026logo=Github)](https://jotrockenmitlocken.de)\r\n[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Kataglyphis/MachineLearningAlgorithms/Python%20application%20on%20Windows?label=Windows%20build\u0026logo=Github)](https://jotrockenmitlocken.de)\r\n[![TopLang](https://img.shields.io/github/languages/top/Kataglyphis/MachineLearningAlgorithms)](https://jotrockenmitlocken.de)\r\n[![codecov](https://codecov.io/gh/Kataglyphis/MachineLearningAlgorithms/branch/main/graph/badge.svg?token=ABEPPS3KCJ)](https://codecov.io/gh/Kataglyphis/MachineLearningAlgorithms)\r\n[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.com/donate/?hosted_button_id=BX9AVVES2P9LN)\r\n[![Twitter](https://img.shields.io/twitter/follow/Cataglyphis_?style=social)](https://twitter.com/Cataglyphis_)\r\n[![YouTube](https://img.shields.io/youtube/channel/subscribers/UC3LZiH4sZzzaVBCUV8knYeg?style=social)](https://www.youtube.com/channel/UC3LZiH4sZzzaVBCUV8knYeg)\r\n\r\n\u003ch4 align=\"center\"\u003e Playground for various ML algorithms \u003ca href=\"https://jotrockenmitlocken.de/\" target=\"_blank\"\u003e\u003c/a\u003e.\u003c/h4\u003e\r\n\r\n\u003c!-- \u003cp align=\"center\"\u003e\r\n  \u003ca href=\"https://paypal.me/JonasHeinle?locale.x=de_DE\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/badge/$-donate-ff69b4.svg?maxAge=2592000\u0026amp;style=flat\"\u003e\r\n  \u003c/a\u003e\r\n\u003c/p\u003e --\u003e\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003ca href=\"#key-features\"\u003eKey Features\u003c/a\u003e •\r\n  \u003ca href=\"#how-to-use\"\u003eHow To Use\u003c/a\u003e •\r\n  \u003ca href=\"#download\"\u003eDownload\u003c/a\u003e •\r\n  \u003ca href=\"#related\"\u003eRelated\u003c/a\u003e •\r\n  \u003ca href=\"#license\"\u003eLicense\u003c/a\u003e •\r\n  \u003ca href=\"#literature\"\u003eLiterature\u003c/a\u003e\r\n\u003c/p\u003e\r\n\r\n\u003c!-- TABLE OF CONTENTS --\u003e\r\n\u003cdetails open=\"open\"\u003e\r\n  \u003csummary\u003eTable of Contents\u003c/summary\u003e\r\n  \u003col\u003e\r\n    \u003cli\u003e\r\n      \u003ca href=\"#about-the-project\"\u003eAbout The Project\u003c/a\u003e\r\n      \u003cul\u003e\r\n        \u003cli\u003e\u003ca href=\"#built-with\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\r\n      \u003c/ul\u003e\r\n      \u003cul\u003e\r\n        \u003cli\u003e\u003ca href=\"#key-features\"\u003eKey Features\u003c/a\u003e\u003c/li\u003e\r\n      \u003c/ul\u003e\r\n    \u003c/li\u003e\r\n    \u003cli\u003e\r\n      \u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e\r\n      \u003cul\u003e\r\n        \u003cli\u003e\u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\r\n        \u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\r\n      \u003c/ul\u003e\r\n    \u003c/li\u003e\r\n    \u003cli\u003e\r\n      \u003ca href=\"#roadmap\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\r\n      \u003cul\u003e\r\n        \u003cli\u003e\u003ca href=\"#environment\"\u003epip/conda environment\u003c/a\u003e\u003c/li\u003e\r\n        \u003cli\u003e\u003ca href=\"#vae\"\u003eVAE\u003c/a\u003e\u003c/li\u003e\r\n        \u003cli\u003e\u003ca href=\"#image segmentation\"\u003eImage Segmentation\u003c/a\u003e\u003c/li\u003e\r\n      \u003c/ul\u003e\r\n    \u003cli\u003e\r\n      \u003ca href=\"#roadmap\"\u003eTests\u003c/a\u003e\u003c/li\u003e\r\n      \u003cul\u003e\r\n        \u003cli\u003e\u003ca href=\"#environment\"\u003eCode Coverage\u003c/a\u003e\u003c/li\u003e\r\n      \u003c/ul\u003e\r\n    \u003cli\u003e\u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\r\n    \u003cli\u003e\u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\r\n    \u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\r\n    \u003cli\u003e\u003ca href=\"#resources\"\u003eResources\u003c/a\u003e\u003c/li\u003e\r\n    \u003cli\u003e\u003ca href=\"#literature\"\u003eLiterature\u003c/a\u003e\u003c/li\u003e\r\n  \u003c/ol\u003e\r\n\u003c/details\u003e\r\n\r\n\u003c!-- ABOUT THE PROJECT --\u003e\r\n## About The Project\r\n\r\nPlayground for a variety of different ML algorithms\r\n\r\n### Key Features\r\n\r\n\u003c!-- ❌ --\u003e\r\n|          Feature                                      |   Implement Status | \r\n| --------------------------------                      | :----------------: | \r\n| VAE                                                   |         ✔️         |\r\n| LSTM                                                  |         ✔️         |\r\n| Image Segmentation with U-Net                         |         ✔️         |\r\n| Object Detection                                      |         ✔️         |\r\n| Face similarity measure                               |         ✔️         |\r\n| Linear Regression                                     |         ✔️         |\r\n| Linear Classification                                 |         ✔️         |\r\n| Naive Bayes Classifier                                |         ✔️         |\r\n| Multiclass Classification                             |         ✔️         |\r\n| k-NN                                                  |         ✔️         |\r\n| Forests                                               |         ✔️         |\r\n| Kernel Ridge Regression                               |         ✔️         |\r\n| Kernelized Support Vector Machine                     |         ✔️         |\r\n| Bayesian Linear Regression                            |         ✔️         |\r\n| Gaussian Processes                                    |         ✔️         |\r\n| k-Means                                               |         ✔️         |\r\n| Expectation Maximization for Gaussian Mixture Models  |         ✔️         |\r\n| Probabilistic PCA with Expectation Maximization       |         ✔️         |\r\n| Neural Network Classifier from Scratch                |         ✔️         |\r\n| Probabilistic PCA with Expectation Maximization       |         ✔️         |\r\n\r\n### Built With\r\n\r\nFor a detailed list of all required packages have a look into the respective environment.yaml. Each project uses its own environment in its own sub directory. \u003cbr/\u003e\r\nIn the following an list (with its purpose):\r\n\r\n* [Python](https://www.python.org/)\r\n* [Jupyter Notebook](https://jupyter.org/)\r\n* [CuPy](https://cupy.dev/)\r\n* [Cuda](https://developer.nvidia.com/cuda-zone)\r\n* [TensorFlow](https://www.tensorflow.org/)\r\n* [Anaconda](https://www.anaconda.com/products/distribution) cross platform packet manager for python\r\n* [pytest](https://docs.pytest.org/en/7.1.x/getting-started.html#get-started) python testing framework\r\n* [Github Actions: setup-miniconda](https://github.com/marketplace/actions/setup-miniconda)\r\n* [flake8](https://flake8.pycqa.org/en/latest/) static code analysis for python\r\n\u003c!-- GETTING STARTED --\u003e\r\n## Getting Started\r\n\r\n### Prerequisites\r\n\r\n[Python](https://www.python.org/) 3.8 \u003cbr /\u003e\r\nFor the dependencies on python modules I created an anaconda environment.yaml file. You can import it in your anaconda project.\u003cbr /\u003e\r\nFor some projects you should have a potent graphics card. (I test code under RTX 2060 SUPER and RTX 3060 Notebook GPU)\r\n\r\n\r\n### Installation\r\n\r\n1. Clone the repo\r\n\r\n    ```sh\r\n    git clone git@github.com:Kataglyphis/MachineLearningAlgorithms.git\r\n    ```\r\n2. Consider using some form of packet management/-distribution software. I am using       here  [Anaconda](https://www.anaconda.com/products/distribution). If you also do so you can use my [Anaconda Env .yml](Documents/anaconda/environment.yaml) for getting all python module dependencies. Keep in mind every project uses its own environment!\u003cbr/\u003e\r\n    ```sh\r\n    conda env create -f environment_PROJECT_TOKEN.yaml \r\n    ```\r\n  \r\n\u003c!-- USAGE EXAMPLES --\u003e\r\n## Usage\r\n\r\n### pip/conda environment\r\n\r\nI use this command to generate cross-platform env.yaml files.\r\n```sh\r\n    conda env export --from-history\u003eenvironment.yaml\r\n   ```\r\n\r\nsee also: [Anaconda entry on environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#sharing-an-environment).\r\n\r\n### VAE\r\n\r\n```sh\r\n    # for training VAE\r\n    python train_vae.py train\r\n    # checking the gradients\r\n    python train_vae.py gradcheck\r\n    #generating sample\r\n    python train_vae.py sample\r\n   ```\r\n\r\n\u003ch1 align=\"center\"\u003e\r\n  \u003cbr\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/Screenshot_2.png\" alt=\"MLAlgo\" width=\"300\"\u003e\u003c/a\u003e\r\n  \u003cbr\u003e\r\n\u003c/h1\u003e\r\n\r\n### LSTM \r\n\r\n\r\n```sh\r\n    # for training VAE\r\n    python lstm_cupy.py train\r\n    # checking the gradients\r\n    python lstm_cupy.py gradcheck\r\n    #generating sample\r\n    python lstm_cupy.py sample sample\r\n```\r\n\r\n---\r\n**NOTE**\r\nUnder folder \"data/\" one can place any kind of .txt file one want to train the net with.\r\nJust make sure it is a .txt!\r\n---\r\n\r\n```\r\nFrom Kants critique of practical reason I got sentences like:\r\n\r\n(emb_size = 16\r\nhidden_size = 124\r\nseq_length = 512\r\nlearning_rate = 5e-2 \r\nmax_updates = 500000\r\nbatch_size = 32)\r\n\r\n1.Result:\r\n\"on this it thush that determinated and representakoug advantaresuct\r\nform) usefing\r\non which there dayver in this made of us as all in our cases of a rat just on it were orle freptive the law is of it. Thus to formalore this serucion the vatures,\r\nthe conding our explesed the vater, he constadnes and only a necessare the hedution now thisk the conduapo-sted anduredsifinated are\r\nlastingavt one of thisplyssul supposables outisaty his\r\noblige of us. Forble only a have feeling to it it itself vione in it were is can in it were in this\r\ndoublious merew, justifysero anderedores in our vabutualy as a new this\r\ncondualed as alwagation as a\r\ncateg- will gerery give\r\nus\r\nall the fationed as all caneon constanally a universal empithed for such it it be advantaking on it whrows for\r\nnot of in this\r\nsured, or beings have or private it it it tho advinated in a noursinedmegules determinour now on it. Thus, fromnevery or in a prove of thishity or if and it of a raterfined to formare of this deterd no douneablentonedered it come is a higher deterd of this dul to high. Now\r\nits conduaventi;ethul frequently screp; this\r\nsuterninamersthicatedmentare the om its oblinately is a higherw\r\nsomethicated as alwaysion firmtom constionchilesogich is in this\r\ndodevere\r\ncomposenery\r\nthind the bas ratherw\r\nhablog constioning virtuest trued it out to\r\nformapo,\r\nadvant well of and chereng in its or if thishs in our causaly his\r\nerred oursualing the\r\nablegt of would only a universul the formais or segve\r\nfrounds thishigances the vatherw\r\nthe bat the determination of to its or in it because the caterfinateorangionation with things the deduct\r\nof morality this dod alonestimateneables, notions our ear\r\nof it a had of us a had only only a law with the\r\nbein the in\r\nhapply to finds to\r\nacu thereon whiculain he can physist to this dut all the vated, however, of it it destricest, and man's onowledgringuit is nothing\r\nan every us to fog mo,ing on its own adept only a reasonally andering the\r\ntysted a universublestlying the bound fromnes\"  \r\n\r\n```\r\n\r\n### Image Segmentation\r\n\r\nThere is a high demand in image segmentation applications.\r\nWith this project you get access to a tool enabling you to build such.\r\n\r\n\u003ch1 align=\"center\"\u003e\r\n  \u003cbr\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"images/SkinDetection.png\" alt=\"MLAlgo\" width=\"300\"\u003e\u003c/a\u003e\r\n  \u003cbr\u003e\r\n\u003c/h1\u003e\r\n\r\n### Object Detection\r\n\r\n### Face similarity measure\r\n\r\n## Tests\r\n\r\n### Code Coverage\r\n* [Watch test results here](Documents/googletest/test_detail.xml)\r\n\u003ch3\u003eCode coverage results\u003c/h3\u003e\r\n\u003ch1 align=\"center\"\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"https://codecov.io/gh/Kataglyphis/MachineLearningAlgorithms/branch/main/graphs/sunburst.svg?token=ABEPPS3KCJ\" alt=\"ML\" width=\"350\"\u003e\u003c/a\u003e\r\n  \u003ca href=\"https://jotrockenmitlocken.de\"\u003e\u003cimg src=\"https://codecov.io/gh/Kataglyphis/MachineLearningAlgorithms/branch/main/graphs/tree.svg?token=ABEPPS3KCJ\" alt=\"ML\" width=\"350\"\u003e\u003c/a\u003e\r\n\u003c/h1\u003e\r\n\r\n\u003c!-- CONTRIBUTING --\u003e\r\n## Contributing\r\n\r\nContributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.\r\n\r\n1. Fork the Project\r\n2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)\r\n3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)\r\n4. Push to the Branch (`git push origin feature/AmazingFeature`)\r\n5. Open a Pull Request\r\n\r\n\u003c!-- LICENSE --\u003e\r\n## License\r\n\r\nDistributed under the GPL-3.0 License. See `LICENSE` for more information.\r\n\r\n\u003c!-- CONTACT --\u003e\r\n## Contact\r\n\r\nJonas Heinle - [@Cataglyphis_](https://twitter.com/Cataglyphis_) - jonasheinle@googlemail.com\r\n\r\nProject Link: [https://github.com/Kataglyphis/MachineLearningAlgorithms](https://github.com/Kataglyphis/MachineLearningAlgorithms)\r\n\r\n## Resources\r\nFor datasets \u003cbr/\u003e\r\n* [Googles Dataset search](https://datasetsearch.research.google.com/)\r\n* [Kaggle](https://www.kaggle.com/)\r\n* [ImageNet](https://image-net.org/)\r\n* [ImageCLEF](https://www.imageclef.org/datasets)\r\n* [OxfordRobotics](https://www.robots.ox.ac.uk/~vgg/data/)\r\n* [COCO Dataset](https://cocodataset.org/#home)\r\n* [UCI Dataset](https://archive.ics.uci.edu/ml/datasets.php)\r\n* [List on wikipedia](https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research)\r\n* [govdata](https://www.govdata.de/)\r\n* [Statista](https://de.statista.com/)\r\n* [DataFlair](https://data-flair.training/blogs/machine-learning-datasets/)\r\n* [SkiKitLearn Datasets](https://scikit-learn.org/stable/datasets) \r\n\r\n## Literature\r\nAnaconda\r\n* [cheatsheet](https://docs.conda.io/projects/conda/en/latest/_downloads/843d9e0198f2a193a3484886fa28163c/conda-cheatsheet.pdf)\r\n\r\nMachine Learning Literature\r\n* [The Matrix Cookbook](https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf)\r\n* [Deep Learning Book](https://www.deeplearningbook.org/)\r\n \r\nDatasets\r\n* [pytorch datasets](https://github.com/pytorch/vision)\r\n\r\nVAE\r\n* [Diederik P Kingma and Max Welling. 2013. Autoencoding variational bayes](https://arxiv.org/abs/1312.6114)\r\n* [quanpn90](https://github.com/quanpn90/VAEAssignment-DLNN2020)\r\n\r\nLSTM \r\n* [karpathy](https://github.com/karpathy/char-rnn)\r\n* [quanpn90](https://github.com/quanpn90/LSTMAssignment-DLNN2020)\r\n\r\nRNN\r\n* [karpathy](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\r\n\r\nImage Segmentation\r\n* [U-Net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/)\r\n\r\n\u003c!-- MARKDOWN LINKS \u0026 IMAGES --\u003e\r\n\u003c!-- https://www.markdownguide.org/basic-syntax/#reference-style-links --\u003e\r\n[contributors-shield]: https://img.shields.io/github/contributors/othneildrew/Best-README-Template.svg?style=for-the-badge\r\n[contributors-url]: https://github.com/othneildrew/Best-README-Template/graphs/contributors\r\n[forks-shield]: https://img.shields.io/github/forks/othneildrew/Best-README-Template.svg?style=for-the-badge\r\n[forks-url]: https://github.com/othneildrew/Best-README-Template/network/members\r\n[stars-shield]: https://img.shields.io/github/stars/othneildrew/Best-README-Template.svg?style=for-the-badge\r\n[stars-url]: https://github.com/othneildrew/Best-README-Template/stargazers\r\n[issues-shield]: https://img.shields.io/github/issues/othneildrew/Best-README-Template.svg?style=for-the-badge\r\n[issues-url]: https://github.com/othneildrew/Best-README-Template/issues\r\n[license-shield]: https://img.shields.io/github/license/othneildrew/Best-README-Template.svg?style=for-the-badge\r\n[license-url]: https://github.com/othneildrew/Best-README-Template/blob/master/LICENSE.txt\r\n[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge\u0026logo=linkedin\u0026colorB=555\r\n[linkedin-url]: https://www.linkedin.com/in/jonas-heinle-0b2a301a0/","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkataglyphis%2Fmachinelearningalgorithms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkataglyphis%2Fmachinelearningalgorithms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkataglyphis%2Fmachinelearningalgorithms/lists"}