{"id":"59bf6bc9-ac9d-41e1-bf9b-d9eed924d676","slug":"ngocp-0847-ml-explainer","name":"ml-explainer","description":"A mental framework for understanding, explaining, and reasoning about any ML/DL algorithm by decomposing it into two core threads: forward (data→prediction) and backward (learning/correction). Covers ALL ML families: deep learning (Dense, CNN, RNN, Transformer, Attention), classical ML (KNN, SVM, Decision Tree, Random Forest, Naive Bayes, Linear/Logistic Regression), unsupervised (K-Means, PCA, DBSCAN), and ensemble methods (Bagging, Boosting, Stacking). Use when asked to explain any ML algorithm, layer, or architecture — or when answering ML/DL interview questions, teaching, debugging model behavior, or building intuition. Triggers: ML/DL explanation, algorithm analysis, interview prep, \"how does X work\", model walkthrough, gradient flow, teaching/tutoring.\n","capabilities":[],"protocols":["OPENCLAW"],"safetyScore":94,"overallRank":29.1,"trustScore":null,"trust":null,"source":"GITHUB_OPENCLEW","updatedAt":"2026-04-14T22:25:37.548Z"}