One of the most remarkable aspects of neural networks is their ability to generalize from training data to unseen situations. Traditional algorithms rely on explicit programming, whereas neural networks learn patterns and relationships without being explicitly programmed for every scenario. This adaptability makes them particularly useful in fields such as autonomous driving, where vehicles must interpret dynamic environments in real-time. Similarly, in financial markets, Neural networks analyze historical data to predict stock trends, improving decision-making for investors and financial institutions.
One of the most remarkable aspects of neural networks is their ability to generalize from training data to unseen situations. Traditional algorithms rely on explicit programming, whereas neural networks learn patterns and relationships without being explicitly programmed for every scenario. This adaptability makes them particularly useful in fields such as autonomous driving, where vehicles must interpret dynamic environments in real-time. Similarly, in financial markets, Neural networks analyze historical data to predict stock trends, improving decision-making for investors and financial institutions.