Neural Network Parameter Growth

Interactive demonstration: Two different approaches to solve the same image classification task

🎯 Task: Image Classification

Same input: 224×224 RGB image → Same output: 10 classes
Different approaches: How to process the image?

🏗️Dense Network Design

Number of Hidden Layers: 3

Neurons per Hidden Layer: 256

🖼️Input Image (Both Networks)

Image Size (width = height): 224

Color Channels: 3 (RGB)

🧠 Approach 1: Dense Neural Network

Flatten image → Connect every pixel to every neuron

Input size: -
Total connections: -

🔍 Approach 2: Convolutional Neural Network

Use local filters → Shared weights → Dense classification

Conv layers: 3×3 kernels, 32→64→128 filters
Max pooling after each conv layer
Adaptive dense layer based on feature count
Final dense: -

📊 Efficiency Comparison

Dense Network
-

CNN
1.0×

🎯 CNNs are incredibly parameter-efficient for image processing!
Dense networks connect every input pixel to every neuron, while CNNs use shared weights and local receptive fields.

Interactive slides designed for enhanced learning experience