
NN#2 — Neural Networks Decoded: Concepts Over Code
Last Updated on February 7, 2025 by Editorial Team
Author(s): RSD Studio.ai
Originally published on Towards AI.
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In our first article, “From Neurons to Networks: A Conceptual Birth of Artificial Intelligence”, we explored the fundamental building block of neural networks — the perceptron. We saw how this simple, neuron-inspired model could make decisions by weighing inputs, summing them, and then “firing” or not, based on a threshold. We introduced the perceptron as a digital mimic of a biological neuron, capable of learning basic patterns and classifications.
But the story doesn’t end with a single perceptron. While remarkably insightful as a starting point, a lone perceptron possesses a fundamental limitation: it operates in a linear world. To truly unlock the power of neural networks and tackle the complexities of real-world data, we need to venture beyond linearity and embrace the world of non-linear functions. This is where the concept of stacking perceptrons comes into play, giving birth to the field of deep learning.
No, Not by a Long Shot!
medium.com
The Perceptron: A Straight Line in a Complex World (A Quick Recap)
Let’s briefly revisit the perceptron’s nature. At its core, a perceptron performs a simple calculation: it takes inputs, multiplies them by weights, adds a bias, and then… Read the full blog for free on Medium.
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