How Deep Learning Solves Complex Data Representation: A Practical Exploration with OR, AND, and XOR Gates
In the world of machine learning, there are some problems that are easy to solve, while others require a deeper understanding. This distinction between simple and complex problems is a key reason why deep learning emerged. In this article, we’ll explore how simple machine learning models struggle with certain problems and how deep learning steps in to save the day.
Let’s dive into it using an intuitive example: solving logic gates such as AND, OR, and XOR. We’ll walk through these gates step-by-step using perceptrons and see how simple models work for basic problems but fail to handle more complex ones like XOR.
Understanding the Perceptron: The Building Block
A perceptron is the simplest type of artificial neuron and is the foundation of neural networks. It receives inputs, applies weights to them, sums them up, adds a bias, and then passes the result through an activation function (like the step function). Based on the result, the perceptron decides whether to activate (output 1) or not (output 0).
