Backpropagation (short for “backward propagation of errors”) is a key algorithm used in training artificial neural networks. It helps the network adjust its internal parameters (weights and biases) to improve accuracy over time. By comparing the network’s predicted output to the actual target, backpropagation calculates errors and then works backward through the network to update weights in a way that minimizes these errors.

The process consists of two main steps: forward propagation and backward propagation. In forward propagation, the input data passes through layers of neurons, with each layer applying weights and activation functions to generate an output. If the output is incorrect, the backward propagation phase begins. This phase uses a mathematical technique called gradient descent, which calculates how much each weight contributed to the error and adjusts it accordingly to reduce future mistakes.

Backpropagation relies on the chain rule of calculus to efficiently distribute error signals across multiple layers. This allows deep neural networks to learn complex patterns from data, making it the foundation of many AI applications, including image recognition, natural language processing, and game-playing AI.

Without backpropagation, training deep neural networks would be significantly harder and less efficient. While newer optimization techniques have emerged, backpropagation remains one of the most widely used methods for teaching AI systems to improve over time.

If you’re curious about how AI concepts like backpropagation fit into the bigger picture, AI For Everyone by DeepLearning.AI is a great place to start. This beginner-friendly course, taught by Andrew Ng, explains the fundamental ideas behind AI, machine learning, and deep learning in simple terms—no coding required. Whether you’re a business leader, student, or enthusiast, this course helps you understand AI’s impact and how to leverage it effectively. Check out *AI For Everyone to get started!