What are the differences between neural networks and traditional algorithms?

What are the differences between neural networks and traditional algorithms?

Neural networks are designed to recognize patterns and learn from data, while traditional algorithms follow predefined rules.

Can you give an example of a traditional algorithm?

Sure! A classic example is the decision tree algorithm, which splits data into branches based on feature values.

How do neural networks learn from data?

Neural networks learn through a process called training, where they adjust weights based on the error of their predictions.

Which one is better for image recognition?

Neural networks, especially convolutional neural networks (CNNs), are generally more effective for image recognition tasks.

Are there situations where traditional algorithms outperform neural networks?

Yes, traditional algorithms can outperform neural networks in cases with limited data or when interpretability is crucial.