Gulu Guliyev·Sep 25SVD Explained on matrix 2x2, oversimplified with real examplesWhat are vectors?A response icon1A response icon1
Leigh Collier·Sep 3Don’t Let Text Data Blow Up Your Model. TF-IDF and Truncated SVD TutorialNot many people realise this but you favourite LLM can’t actually be your therapist. Because a therapist understand what you mean. You read…
Subhasish Das·Aug 26From Plasma to Thin Film: A Practical Guide to SputteringHow can we create a thin film or coating of any matrial on top of another matrial surface?
Diane Khambu·Aug 17Learning Principal Component Analysis (PCA) on my Own TermsI was a bit surprised that I came across a note on my notebook that had these:
Vira Fitriyani·Jul 13Designing a Three-Tier Cold-Start Strategy with Fold-In Approximation and Online BlendingHow I built an on-device book recommendation engine that gracefully handles the transition from “I know nothing about you” to “I know…
Mathcube·Jul 6SVD: How a Photo Hides Inside 20 PatternsThe singular value decomposition — the workhorse behind image compression and recommenders — read off a single face.A response icon2A response icon2
Sachinkc·Jul 4Learning Recommendation Systems by Building a Hybrid Movie RecommenderHow I built Movie Recommendation system to explore content-based filtering, collaborative filtering, and the machine learning concepts…
Edgar Bellot Mico·Jun 30Predecir bien no es recomendar bien: lo que aprendí construyendo un recomendador con SurpriseUn recorrido práctico por la librería Surprise, desde el RMSE hasta las métricas que de verdad importan, con una vuelta de tuerca sobre…
Max Zhao·Apr 19Understanding SVD through information loss minimizationSVD is one of the fundamental tools in matrix analysis, and it is widely used in areas like data compression and machine learning. In the…
Rahul Singh·Apr 6From Hidden Patterns to Personalized AgentsHow LoRA, LoRe, SVD, and latent vectors all tell the same story
