Question

Understanding Convolutional Neural Networks — a beginner guide

March 7, 2026

COMSATS University Islamabad

Publication ID: pub00000000000000000007

Abstract

I am new to deep learning and want to understand CNNs from scratch. Can someone explain: 1. What makes convolution different from fully connected layers? 2. How does backpropagation work through convolutional layers? 3. What is the intuition behind pooling operations? 4. How many filters should I use in each layer? I understand basic neural networks and have Python/NumPy experience.

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Discussion (3)

Ali Khan Mar 7, 2026 · University of Lahore

Great question! Here is the key insight: convolutional layers share weights spatially, which means the same filter detects the same feature everywhere in the image. A fully connected layer treats every pixel independently — so CNNs are vastly more parameter-efficient for spatial data.

Sara Ahmed Mar 7, 2026 · MIT

For pooling intuition: think of max pooling as saying "did this feature appear anywhere in this region?" It provides translational invariance. Average pooling is gentler but max pooling usually wins in practice for image classification.