How CNNs Find Patterns | CMU Intro to Deep Learning: Lecture 9 | 11-785 | L9 | Explainer

CMU 11-785 Lecture 9 introduces convolutional neural networks through local receptive fields, shared filters, and hierarchical feature extraction. The explainer shows how these design choices differ from a fully connected network when patterns appear in different positions. These architectural ideas explain useful inductive biases; they do not imply that every CNN is automatically invariant or best for every task. ORIGINAL SOURCE Creator: Carnegie Mellon University Deep Learning Title: CMU Introduction to Deep Learning 11785, Spring 2026: Lecture 9 Published: 2026-02-09    • CMU Introduction to Deep Learning 11785, S...   ABOUT SPLAINERS HQ Splainers HQ provides explainers for important discussions happening in the tech world. Independent educational summary; not affiliated with or endorsed by the original creator or speaker. Watch the original for full context. #TechSplainers #DeepLearning #CNN