How does a neural network become smarter and more accurate over time?
By receiving more data and learning from it
Explanation:
A neural network becomes smarter and more accurate by learning from more examples and improving its predictions over time. During training, it analyzes data, identifies mistakes, and adjusts its internal parameters (weights and biases) to improve performance.
Why not the others?
- b) By having more hidden layers → More layers can help with complex tasks, but they do not automatically make the model smarter.
- c) By using activation functions → Activation functions help the network learn complex patterns, but they do not directly improve accuracy by themselves.
- d) By requiring less computation power → Lower computation does not make a neural network more accurate.
Simple example:
A facial recognition model becomes better by seeing and learning from thousands or millions of face images, allowing it to recognize faces more accurately.
