This is the Linux app named Machine Learning Cheat Sheet whose latest release can be downloaded as machine-learning-cheat-sheetsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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Urmați aceste instrucțiuni pentru a rula această aplicație:
- 1. Ați descărcat această aplicație pe computer.
- 2. Introduceți în managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator pe care îl doriți.
- 3. Încărcați această aplicație într-un astfel de manager de fișiere.
- 4. Porniți emulatorul online OnWorks Linux sau Windows online sau emulatorul online MACOS de pe acest site web.
- 5. Din sistemul de operare OnWorks Linux pe care tocmai l-ați pornit, accesați managerul nostru de fișiere https://www.onworks.net/myfiles.php?username=XXXXX cu numele de utilizator dorit.
- 6. Descărcați aplicația, instalați-o și rulați-o.
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Fișă informativă despre învățarea automată
DESCRIERE:
This repository is a visually rich and well-organized “cheat sheet” summarizing core machine learning concepts, algorithms, formulas, and best practices. It includes summaries of supervised and unsupervised learning methods, model evaluation metrics (accuracy, precision, recall, ROC/AUC), overfitting/underfitting, regularization (L1/L2), cross-validation, feature engineering techniques, and perhaps tips for hyperparameter tuning. Each section is presented concisely, often with diagrams, formula snippets, and short explanatory notes to serve as quick reference for students, practitioners, or interview prep. The repository is ideal for those who want a compact, at-a-glance reminder of ML fundamentals without diving back into textbooks. Because the cheat sheet is meant to be portable and broadly useful, it is format-friendly (often in Markdown, PDF, or image formats) and easy to include in learning workflow or slides.
Categorii
- Compact summary of core supervised and unsupervised algorithms
- Key formulas and metrics (loss functions, ROC/AUC, confusion matrix, regularization)
- Visual diagrams illustrating model behavior or tradeoffs
- Feature engineering, validation, and hyperparameter tuning tips
- Community contributions and versioning for updates
- Multi-format availability (Markdown / PDF / image) for portability
Categorii
This is an application that can also be fetched from https://sourceforge.net/projects/machine-learning-cheat.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.