



How does the complexity of a perceptron model relate to the bias-variance trade-off? What are ensemble methods, and can they be used with perceptrons?
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what is perceptron Informational
- Can a network of perceptrons represent any Boolean function?
- What is dropout, and how does it help in training deep neural networks? Is a single perceptron considered a neural network?
- What is the history of the perceptron model? Who developed the first perceptron model?
- What are some common kernel functions used with perceptron-like models?
- How does the choice of activation function impact the training dynamics? What is the role of the output layer in determining the type of problem a perceptron can solve (e.g., binary classification, multi-class classification, regression)?
- What is a linearly separable problem? Can a single-layer perceptron solve non-linearly separable problems?
- What are some variations of the ReLU activation function (e.g., Leaky ReLU, Parametric ReLU)?
- What is the role of misclassified examples in the perceptron learning rule?
- How does the bias term shift the decision boundary? Illustrate the decision boundary of a perceptron with a simple example in 2D.
- What are some examples of hybrid neural network architectures?
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