Neural Network (NN)

A neural network (NN) is a machine learning model inspired by how the human brain processes information. It is made up of simple units called neurons (or nodes) that are connected to each other and arranged in layers. Each neuron receives input, applies a mathematical operation, and passes the result forward, allowing the network to learn patterns and relationships in data.

Neural networks usually have three main parts.

• an input layer that receives raw data;
• one or more hidden layers that transform this data; and 
• an output layer that produces the final result.

The connections between neurons have weights, which control how much influence one neuron has on another. During training, these weights are adjusted so the network’s predictions get closer to the correct answers.

To learn, neural networks compare their predictions with actual outcomes using a loss function, then update their weights through a process called backpropagation. Over time, this helps the model generalize, meaning it can make useful predictions on new, unseen data.

Neural networks are a foundation of modern AI and are used in tasks like image recognition, speech recognition, natural language processing, forecasting, and recommendation systems.