Neural Network

Overview

A computational model inspired by the structure and function of biological neural networks. It consists of interconnected nodes, or "neurons," organized in layers. Each connection between neurons has an associated weight that is adjusted during model training. Neural networks are capable of learning complex patterns and relationships in data, making them a powerful tool for various machine learning tasks, especially deep learning, where multi-layered structures have led to very high performance.

What is a Neural Network?

Neural networks are computational systems that mimic the brain's structure:

• Interconnected artificial neurons form the basic units
• Multiple layers process information hierarchically
• Weighted connections between neurons store learned patterns
• Input layer receives data, hidden layers process it, output layer produces results

How Do Neural Networks Learn?

• Adapts through exposure to training data
• Adjusts connection weights iteratively
• Uses backpropagation for optimization
• Learns patterns automatically from examples
• Improves with more relevant data
• Generalizes learning to new situations

Key Applications

• Pattern recognition and classification
• Image and speech processing
• Natural language understanding
• Complex decision-making systems
• Predictive modeling and forecasting
• Automated feature extraction

Related Concepts

• Deep Learning
• Machine Learning
• Backpropagation
• Model Training
• Loss Function
• Gradient Descent