NN#11 Neural Networks Decoded: Concepts Over Code 0 like March 7, 2025Share this postAuthor(s): RSD Studio.ai Originally published on Towards AI. Limitations of ANNs: Move to Convolutional Neural NetworksThis member-only story is on us. Upgrade to access all of Medium.The journey from traditional neural networks to convolutional architectures wasnt 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.CNNs Visualized By CudoComputeIf you have not read my previous articles of this series, do give it a read to understand ANNs:RSD Studio.aiView list10 storiesTraditional 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 werent subtle they were systemic and severe.Consider this: a modest 200200 pixel grayscale image contains 40,000 individual values. Color that image with RGB channels, and youre 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.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post