Understanding Neural Networks: From Perceptrons to Deep Learning
Neural networks are at the heart of modern artificial intelligence (AI) and machine learning (ML). Their ability to model complex patterns and relationships has revolutionized fields ranging from image recognition to natural language processing. But how did we get here? Understanding neural networks involves tracing their evolution from simple perceptrons to sophisticated deep learning architectures. In this blog, we'll explore the journey of neural networks, examining their fundamental concepts and how they've evolved over time. 1. The Dawn of Neural Networks: Perceptrons The concept of neural networks dates back to the 1950s, with the introduction of the perceptron by Frank Rosenblatt. A perceptron is the simplest type of artificial neural network and represents the earliest attempt to simulate a human brain's decision-making process. The Perceptron basics are as follows 1) Structure : A single-layer perceptron consists of input nodes, weights, a summation function,