What are Neural Networks?

Neural networks are computing systems inspired by the structure of the human brain. They are designed to recognize patterns and solve complex problems by processing information through layers of connected “neurons.”

A neural network consists of three main layers:

• Input layer – Receives data (such as images or text).
  
  

• Hidden layers – Process the data through mathematical calculations.
  
  

• Output layer – Produces the final result or prediction.
  
  

Each connection between neurons has a value called a weight, which adjusts as the network learns. During training, the network compares its predictions to correct answers and adjusts weights to improve accuracy. This process is called backpropagation.

Neural networks are especially powerful for tasks like image recognition, speech processing, translation, and AI language models. Deep neural networks with many hidden layers are known as deep learning models.

Because neural networks can process large amounts of data efficiently, they are widely used in AI systems such as facial recognition and self-driving technology.

In short, neural networks are mathematical models that simulate brain-like learning to recognize patterns and make predictions.