What is the exploding gradient problem?
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what is perceptron Informational
- What are the trade-offs between linear models like basic perceptrons and non-linear models with kernel functions or multiple layers? How does the choice of distance metric affect the performance of nearest neighbor algorithms compared to perceptrons?
- What are the connections between information theory and the design of optimal classifiers? How can the concept of entropy be used to evaluate the performance or guide the training of a perceptron?
- How can the principles of causality be integrated with perceptron-based models? What are the potential applications of causal inference in improving the robustness and interpretability of AI systems?
- How does feature scaling affect the convergence of the perceptron learning algorithm? What is the curse of dimensionality, and how does it affect perceptrons and MLPs?
- Explain the concept of receptive field in convolutional neural networks and how it relates to the input of a perceptron-like neuron. How do pooling layers in CNNs reduce dimensionality and provide translation invariance?
- What is concept drift, and how can it affect a deployed perceptron model?
- Explain the concept of local minima in the context of training neural networks.
- How can we ensure fairness and accountability in AI systems based on perceptrons?
- What is the bias-variance trade-off in machine learning models?
- How is the test set used to evaluate the final performance of a trained perceptron? Explain the difference between a parameter and a hyperparameter in a perceptron model.
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