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.
Download and run online this app named Machine Learning Cheat Sheet with OnWorks for free.
Suivez ces instructions pour exécuter cette application :
- 1. Téléchargé cette application sur votre PC.
- 2. Entrez dans notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous voulez.
- 3. Téléchargez cette application dans ce gestionnaire de fichiers.
- 4. Démarrez l'émulateur en ligne OnWorks Linux ou Windows en ligne ou l'émulateur en ligne MACOS à partir de ce site Web.
- 5. Depuis le système d'exploitation OnWorks Linux que vous venez de démarrer, accédez à notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous souhaitez.
- 6. Téléchargez l'application, installez-la et exécutez-la.
CAPTURES D'ÉCRAN:
Aide-mémoire sur l'apprentissage automatique
DESCRIPTION:
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.
Caractéristiques
- 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
Catégories
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.