NN#10 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

Neural Networks Competition Over the Years

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The evolution of artificial neural networks (ANNs) resembles less a steady march forward and more a complex ecosystem of competing species — each architecture rising, dominating, and sometimes fading as computational landscapes shift. This isn’t just about algorithmic improvements; it’s about fundamental transformations in how machines perceive and process our world.

Let’s journey through the neural architecture zoo that has defined machine learning’s trajectory over the decades.

This article will explore developments from basic ANNs that we have studied till now to current state of the art paving way for the next part of the series. If you haven’t read the previous series, do give it a read:

“From Neurons to Networks: A Conceptual Birth of Artificial Intelligence”

pub.towardsai.net

The McCulloch-Pitts Neuron (1943) emerged as our first formal model of a neural processing unit. Simple by today’s standards, it represented a binary threshold logic that captured the fundamental insight: networks of simple computational units could theoretically compute anything.

But theory needed implementation, and the Perceptron (1958) delivered. Frank Rosenblatt’s creation could actually learn from data, adjusting weights through a simple algorithm. The excitement was palpable — until Minsky and Papert’s infamous 1969 book “Perceptrons” demonstrated… Read the full blog for free on Medium.

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Published via Towards AI