NN#11 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

Limitations of ANNs: Move to Convolutional Neural Networks

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The journey from traditional neural networks to convolutional architectures wasn’t just a technical evolution — it was a fundamental reimagining of how machines should perceive visual information. This shift represents one of the most consequential pivots in AI history, one that ultimately unlocked the door to machine vision as we know it today.

If you have not read my previous articles of this series, do give it a read to understand ANNs:

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Traditional Artificial Neural Networks (ANNs) showed impressive capabilities with structured data, but they hit a wall when confronted with the rich complexity of visual information. The limitations weren’t subtle — they were systemic and severe.

Consider this: a modest 200×200 pixel grayscale image contains 40,000 individual values. Color that image with RGB channels, and you’re suddenly managing 120,000 input neurons. The computational requirements grow exponentially with image resolution, creating a perfect storm of challenges:

A fully-connected network processing 1080p images would require approximately 6 million neurons in the input layer alone. Each connection demands a weight parameter — multiplying this across a mere 1,000 hidden neurons would result in 6 billion parameters for just the… Read the full blog for free on Medium.

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Published via Towards AI