NN#2 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

This member-only story is on us. Upgrade to access all of Medium.

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.

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 a sponsor.

Published via Towards AI