{"id":13544487,"url":"https://github.com/aws-samples/aws-mlu-explain","last_synced_at":"2025-04-14T08:53:29.474Z","repository":{"id":38234919,"uuid":"416921348","full_name":"aws-samples/aws-mlu-explain","owner":"aws-samples","description":"Visual, Interactive Articles About Machine Learning:  https://mlu-explain.github.io/","archived":false,"fork":false,"pushed_at":"2024-11-12T14:13:22.000Z","size":67364,"stargazers_count":698,"open_issues_count":8,"forks_count":94,"subscribers_count":20,"default_branch":"main","last_synced_at":"2025-04-07T02:06:30.508Z","etag":null,"topics":["ai","aws","d3","datavisualization","dataviz","deep-learning","machine-learning","machinelearning","mlu","svelte"],"latest_commit_sha":null,"homepage":"https://mlu-explain.github.io/","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aws-samples.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-10-13T22:58:27.000Z","updated_at":"2025-04-07T01:07:42.000Z","dependencies_parsed_at":"2024-11-30T10:21:44.996Z","dependency_job_id":null,"html_url":"https://github.com/aws-samples/aws-mlu-explain","commit_stats":{"total_commits":449,"total_committers":13,"mean_commits":34.53846153846154,"dds":0.3986636971046771,"last_synced_commit":"1ef330e547786940623b4016dd278d46befba691"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws-samples%2Faws-mlu-explain","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws-samples%2Faws-mlu-explain/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws-samples%2Faws-mlu-explain/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws-samples%2Faws-mlu-explain/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aws-samples","download_url":"https://codeload.github.com/aws-samples/aws-mlu-explain/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248852107,"owners_count":21171839,"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":["ai","aws","d3","datavisualization","dataviz","deep-learning","machine-learning","machinelearning","mlu","svelte"],"created_at":"2024-08-01T11:00:49.948Z","updated_at":"2025-04-14T08:53:29.446Z","avatar_url":"https://github.com/aws-samples.png","language":"JavaScript","funding_links":[],"categories":["English"],"sub_categories":[],"readme":"![MLU-Explain Logo \u0026 Title](./assets/readme_header.png)\n\nThis repository holds the code used for Amazon's [MLU-Explain](https://mlu-explain.github.io/) educational articles on machine learning. MLU-Explain exists to illustrate core machine learning concepts using visual essays in a fun, informative, and accessible manner.\n\nThis material exists as supplementary educational material for [Machine Learning University (MLU)](https://aws.amazon.com/machine-learning/mlu/), which provides anybody, anywhere, at any time access to the same machine learning courses used to train Amazon’s own developers on machine learning.\n\n# Articles\n\n## Linear Regression\n\n\u003cimg src=\"./assets/gifs/linear-regression.gif\" alt=\"Linear Regression Article Image\" width=\"400\"/\u003e\n\n**Title**: [Linear Regression](https://mlu-explain.github.io/linear-regression/)\n\n**Summary**: A visual, interactive explanation of linear regression for machine learning.\n\n**Code**: [/code/linear-regression/](/code/linear-regression)\n\n**Authors**: Jared Wilber\n\n## Logistic Regression\n\n\u003cimg src=\"./assets/gifs/logistic-regression.gif\" alt=\"Logistic Regression Article Image\" width=\"400\"/\u003e\n\n**Title**: [Logistic Regression](https://mlu-explain.github.io/logistic-regression/)\n\n**Summary**: Learn about how logistic regression can be used for binary classification through an interactive example.\n\n**Code**: [/code/logistic-regression/](/code/logistic-regression)\n\n**Authors**: Erin Bugbee, Jared Wilber\n\n## ROC \u0026 AUC\n\n\u003cimg src=\"./assets/gifs/roc-auc.gif\" alt=\"ROC \u0026 AUC Article Preview\" width=\"400\"/\u003e\n\n**Title**: [ROC \u0026 AUC](https://mlu-explain.github.io/roc-auc/)\n\n**Summary**: A visual explanation of the Receiver Operating Characteristic Curve (ROC) curve, how it works with a live interactive example, and how it relates to Area Under The Curve (AUC).\n\n**Code**: [/code/roc-auc/](/code/roc-auc)\n\n**Authors**: Jared Wilber\n\n## Train, Test, And Validation Sets\n\n\u003cimg src=\"./assets/gifs/train-test-validation.gif\" alt=\"Train, Test, And Validation Sets Article Image\" width=\"400\"/\u003e\n\n**Title**: [Train, Test, and Validation Sets](https://mlu-explain.github.io/train-test-validation/)\n\n**Summary**: Learn why it is best practice to split your data into training, testing, and validation sets, and explore the utility of each with a live machine learning model.\n\n**Code**: [/code/train-test-validation/](/code/train-test-validation)\n\n**Authors**: Jared Wilber, Brent Werness\n\n## Precision \u0026 Recall\n\n\u003cimg src=\"./assets/gifs/precision-recall.gif\" alt=\"Precision \u0026 Recall Article Preview\" width=\"400\"/\u003e\n\n**Title**: [Precision \u0026 Recall](https://mlu-explain.github.io/precision-recall/)\n\n**Summary**: When it comes to evaluating classification models, accuracy is often a poor metric. This article covers two common alternatives, Precision and Recall, as well as the F1-score and Confusion Matrices.\n\n**Code**: [/code/precision-recall/](/code/precision-recall)\n\n**Authors**: Jared Wilber\n\n## Random Forest\n\n\u003cimg src=\"./assets/gifs/random-forest.gif\" alt=\"Random Forest Article Image\" width=\"400\"/\u003e\n\n**Title**: [Random Forest](https://mlu-explain.github.io/random-forest/)\n\n**Summary**: Learn how the majority vote and well-placed randomness can extend the decision tree model to one of machine learning's most widely-used algorithms, the Random Forest.\n\n**Code**: [/code/random-forest/](/code/random-forest)\n\n**Authors**: Jenny Yeon, Jared Wilber\n\n## Decision Trees\n\n\u003cimg src=\"./assets/gifs/decision-tree.gif\" alt=\"Decision Trees Article Image\" width=\"400\"/\u003e\n\n**Title**: [Decision Trees](https://mlu-explain.github.io/decision-tree/)\n\n**Summary**: Explore one of machine learning's most popular supervised algorithms: the Decision Tree. Learn how the tree makes its splits, the concepts of Entropy and Information Gain, and why going too deep is problematic.\n\n**Code**: [/code/decision-tree/](/code/decision-tree)\n\n**Authors**: Jared Wilber, Lucía Santamaría\n\n## Bias Variance Tradeoff\n\n\u003cimg src=\"./assets/gifs/mlu-explain_biasvariance.gif\" alt=\"Bias Variance Tradeoff Article Image\" width=\"400\"/\u003e\n\n**Title**: [The Bias Variance Tradeoff](https://mlu-explain.github.io/bias-variance/)\n\n**Summary**: Understand the tradeoff between under- and over-fitting models, how it relates to bias and variance, and explore interactive examples related to LASSO and KNN.\n\n**Code**: [/code/bias-variance/](/code/bias-variance)\n\n**Authors**: Jared Wilber, Brent Werness\n\n## Double Descent: A Visual Introduction\n\n\u003cimg src=\"./assets/gifs/double-descent1.gif\" alt=\"Double Descent Article Image\" width=\"400\"/\u003e\n\n**Title**: [Double Descent](https://mlu-explain.github.io/double-descent/)\n\n**Summary**: Meet the double descent phenomenon in modern machine learning: what it is, how it relates to the bias-variance tradeoff, the importance of the interpolation regime, and a theory of what lies behind.\n\n**Code**: [/code/double-descent/](/code/double-descent)\n\n**Authors**: Jared Wilber, Brent Werness\n\n## Double Descent 2: A Mathematical Explanation\n\n\u003cimg src=\"./assets/gifs/double-desent2.gif\" alt=\"Double Descent 2 Article Image\" width=\"400\"/\u003e\n\n**Title**: [Double Descent 2](https://mlu-explain.github.io/double-descent2/)\n\n**Summary**: Deepen your understanding of the double descent phenomenon. The article builds on the cubic spline example introduced in Double Descent 1, describing in mathematical detail what is happening.\n\n**Code**: [/code/double-descent2/](/code/double-descent2)\n\n**Authors**: Brent Werness, Jared Wilber\n\n## Running Locally\n\nThis article holds code for each articles, as well as the generated builds from the code (e.g. the static assets comprising the articles).\n\nFirst, clone this repo.\n\n```bash\ngit clone https://github.com/aws-samples/aws-mlu-explain.git\n```\n\nNext, cd into the article of interest and install the required libraries.\n\n```bash\n# e.g. bias variance tradeoff article\ncd bias-variance\n# install libraries\nnpm install\n```\n\nNow, to run the development version:\n\n```bash\nnpm start\n```\n\nTo build and view the static assests:\n\n```bash\n# build assets\nnpm run build\n# view generated article\ncd dist/\n# run local server\npython3 -m http.server # or just `live-server`\n```\n\n## License Summary\n\nThis open source articles are made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See [LICENSE](LICENSE) file.\n\nThe sample and reference code within this open source book is made available under a modified MIT license. See the [LICENSE-SAMPLECODE](LICENSE-SAMPLECODE) file.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws-samples%2Faws-mlu-explain","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faws-samples%2Faws-mlu-explain","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws-samples%2Faws-mlu-explain/lists"}