Read the below passage and answer the questions. Artificial Neural Networks…
2024
Read the below passage and answer the questions.
Artificial Neural Networks (ANNs) are computational models inspired by the human brain's neural networks. They consist of inter-connected nodes, or neurons, organized into layers: an input layer, one or more hidden layers, and an output layer. Each connection between neurons has a weight that adjusts as learning progresses, allowing the network to adapt and improve its performance. ANNs are particularly effective in recognizing patterns, making them valuable for tasks such as image and speech recognition, Natural language processing, and predictive analytics. Learning in ANNs typically involves training algorithms like backpropagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.
What is the role of weights in an ANN?
- A.
To store data
- B.
To adjust and improve network performance
- C.
To control the speed
- D.
To secure the network.
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Correct answer: B
Answer: Weights determine the strength of connections between neurons and are adjusted during training to improve the network's performance.
Weights scale each input before the values are summed and passed through an activation function; they control how much influence an input has on a neuron's output.
During training, algorithms such as backpropagation update weights to minimize prediction error, enabling the network to learn from data.
By changing weights, the network encodes patterns and relationships in the training data, which improves accuracy on the task.
Weights are learned parameters and are not intended for storing arbitrary data, controlling execution speed, or providing security.
Therefore, the correct description of the role of weights is: they adjust and improve network performance by controlling connection strengths and being updated during learning.