



What are some techniques to improve the training of perceptrons and MLPs (e.g., initialization)? What is the decision boundary of a perceptron?
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what is perceptron Informational
- What are the ethical considerations surrounding the use of massive datasets for training AI systems?
- 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?
- What is information theory, and are there any connections to the design or analysis of perceptrons?
- How does the learning rate affect the convergence of gradient descent?
- What are the computational costs associated with training and using perceptrons and MLPs? How does the size of the network (number of layers and neurons) affect the computational cost?
- 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.
- How can perceptrons be used in robotics and control systems? How can perceptrons be used in healthcare applications?
- What is the relationship between Adaline and linear regression?
- What is the role of weights in a perceptron? What is the bias in a perceptron?
- What are principal component analysis (PCA)? How can PCA be used to reduce the dimensionality of the input data for a perceptron?
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