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Questions and Answers
Which of the following is true about artificial neural networks?
Which of the following is true about artificial neural networks?
- They model connections of biological neurons as weights between nodes (correct)
- They have a linear activation function
- They are only used for predictive modeling
- They cannot be trained via a dataset
What is the acceptable range of output for an artificial neural network?
What is the acceptable range of output for an artificial neural network?
- 1 and 2
- −1 and 0
- 0 and 1, or it could be −1 and 1 (correct)
- 0 and 1
What is the purpose of an activation function in an artificial neural network?
What is the purpose of an activation function in an artificial neural network?
- To control the amplitude of the output (correct)
- To modify the inputs
- To model connections of biological neurons
- To sum the inputs
What does a positive weight in an artificial neural network indicate?
What does a positive weight in an artificial neural network indicate?
What can artificial neural networks be trained via?
What can artificial neural networks be trained via?
Flashcards
What do artificial neural networks model?
What do artificial neural networks model?
Artificial neural networks mimic the connections between biological neurons using weighted connections between nodes.
What's the output range for an artificial neural network?
What's the output range for an artificial neural network?
The output of an artificial neural network typically falls between 0 and 1, though it can also range from -1 to 1.
What does an activation function do in an artificial neural network?
What does an activation function do in an artificial neural network?
Activation functions control the amplitude of the output in an artificial neural network, determining how strongly a neuron fires.
What does a positive weight mean in an artificial neural network?
What does a positive weight mean in an artificial neural network?
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How are artificial neural networks trained?
How are artificial neural networks trained?
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Study Notes
Artificial Neural Networks
- Artificial neural networks can be trained via supervised learning, unsupervised learning, and reinforcement learning.
- The acceptable range of output for an artificial neural network is typically between 0 and 1.
- The purpose of an activation function in an artificial neural network is to introduce non-linearity into the model, allowing it to learn more complex relationships between inputs and outputs.
- A positive weight in an artificial neural network indicates an excitatory connection, meaning the input increases the likelihood of the neuron firing.
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