What are parameters and weights in an AI system?
Asked on Aug 04, 2025
Answer
Parameters and weights are fundamental components of AI systems, particularly in neural networks, where they determine how the input data is transformed into the output. Parameters are the elements of the model that are learned from the training data, and weights are specific types of parameters that adjust the strength of connections between neurons in the network.
Example Concept: In a neural network, weights are numerical values assigned to the connections between neurons. During training, the network adjusts these weights to minimize the difference between the predicted output and the actual output. Parameters, including weights and biases, are updated through optimization algorithms like gradient descent, allowing the model to learn patterns from the data.
Additional Comment:
- Weights determine the importance of input features in predicting the output.
- Biases are another type of parameter that allows the model to shift the activation function.
- Training involves iteratively adjusting weights and biases to improve model accuracy.
- Parameters are crucial for the model's ability to generalize from training data to unseen data.
Recommended Links: