From Perceptrons to Sigmoid Superstars: Building Smarter Neural Networks
Author(s): Hayanan Originally published on Towards AI. Unveiling the Magic of Gradient Descent, Feedforward Architectures, and Universal Function Approximation in AI Neural networks form the backbone of modern artificial intelligence, powering breakthroughs in computer vision, natural language processing, recommender systems, and scientific discovery. Yet beneath today’s deep architectures lie simple mathematical ideas developed decades ago. This article presents a comprehensive, end-to-end journey through the evolution of neural networks from the foundational perceptron to sigmoid neurons, gradient-based learning, feedforward architectures, and the Universal Approximation Theorem. Image credit: upgrad.comThis article traces the evolution of neural networks from perceptrons to sigmoid neurons and feedforward architectures, emphasizing the significance of gradient descent as a learning engine. It illustrates the concepts through historical context, practical examples, and hands-on coding insights while addressing the core principles that have enabled neural networks to progress into effective AI models. 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
