Can you explain neural networks in simple terms?

A neural network is a computational model inspired by the brain’s network of neurons. It consists of layers of interconnected nodes (neurons) that process information. Each neuron receives inputs, multiplies them by weights, adds a bias, and applies an activation function (like ReLU or sigmoid) to produce an output. The network’s layers are usually organized as an input layer, one or more hidden layers, and an output layer. During training, the network adjusts the weights to reduce the difference between its predictions and the true labels, using a method called backpropagation combined with gradient descent. Think of it as a multi‑step filter: raw data enters, passes through hidden layers that extract increasingly abstract features, and finally produces a result such as a class label or a numeric value. Neural networks excel at tasks like image recognition, natural language processing, and time‑series forecasting because they can learn complex, non‑linear relationships directly from data.