How Neural Networks Learn: Activation Functions and Backpropagation
Understanding how neural networks learn is essential to grasping the fundamentals of artificial intelligence and deep learning. At the heart of this process lie two critical components: activation functions and backpropagation . These elements work together to enable neural networks to model complex patterns, make accurate predictions, and continuously improve through training. In this post, we will explore the roles of activation functions and backpropagation, how they work, and why they are essential in the learning process of neural networks. The Role of Activation Functions Activation functions are mathematical equations that determine the output of a neural network node, or “neuron.” They decide whether a neuron should be activated or not by calculating a weighted sum and applying a transformation. Without activation functions, neural networks would simply behave like linear regression models, lacking the ability to model complex data relationships. One of the most common a...