



What are the minimum number of hidden layers and neurons required to solve the XOR problem? Describe the architecture of a multi-layer perceptron for the XOR problem.
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what is perceptron Informational
- How do you initialize the weights of a perceptron?
- What are support vector regression (SVR), and how does it relate to the concepts of perceptrons and regression?
- How do you choose the appropriate architecture for a neural network for a given task? What is the process of hyperparameter tuning for perceptrons and MLPs?
- What is boosting, and how can it be applied to create a strong classifier from multiple weak perceptrons?
- Can a perceptron learn a decision boundary with a large margin?
- What are some common loss functions used in training perceptrons and MLPs? What is the mean squared error (MSE) loss?
- How do you implement and train a perceptron from scratch in Python without using external libraries? What are the mathematical foundations behind the perceptron learning rule and backpropagation?
- What are the trade-offs between the complexity of a perceptron model and its performance?
- What are Hopfield networks, and how do they store and retrieve patterns? What is the relationship between perceptrons and statistical pattern recognition?
- Explain the concept of weight decay in the context of L2 regularization. How does the regularization strength (lambda) affect the amount of regularization?
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