Neural networks are a subset of artificial intelligence (AI) and machine learning that mimic the way the human brain processes information. They consist of interconnected layers of nodes (neurons) that analyze data patterns, enabling tasks such as image recognition, natural language processing, and autonomous decision-making. Neural networks power AI applications in fields such as healthcare, finance, robotics, and predictive analytics.

How Neural Networks Work:
Neural networks process data through multiple layers of artificial neurons that transform inputs into meaningful outputs. Key components include:

Input Layer: Receives raw data (e.g., images, text, numerical values).
Hidden Layers: Apply mathematical operations and transformations to extract patterns and features.
Activation Functions: Determine the strength of neuron connections, allowing non-linearity in decision-making.
Weights & Biases: Adjust over time through training to optimize predictions.
Output Layer: Generates the final prediction or classification based on learned patterns.
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Neural networks improve accuracy through iterative learning, using vast datasets and optimization algorithms such as backpropagation and gradient descent.