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what is perceptron Informational
- How can a multi-layer perceptron be used for multi-class classification? What are different output layer activation functions for multi-class classification (e.g., softmax)?
- What are some frameworks that support distributed training of neural networks?
- How can you combine the predictions of multiple perceptron models in an ensemble? What are some common activation functions used in the hidden layers of MLPs?
- How does the learning rate affect the convergence of gradient descent?
- How can dimensionality reduction techniques be used to gain insights into the internal representations of a perceptron?
- Give examples of problems that are not linearly separable.
- What are some of the limitations of using basic perceptrons for complex NLP and computer vision tasks? How do more advanced neural network architectures address these limitations?
- When is cross-entropy loss preferred over MSE? How does the choice of loss function affect the training process?
- What are some common performance metrics for regression tasks (if a perceptron is adapted for regression)?
- 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?
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