What is the XOR problem, and why is it difficult for a single-layer perceptron?
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what is perceptron Informational
- How is the decision boundary determined by the weights and bias?
- How do you choose an appropriate learning rate? What are learning rate schedules?
- 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?
- What is the effect of a very small or very large learning rate?
- What were the main criticisms and limitations identified in the early days of perceptron research? How did the development of backpropagation contribute to the resurgence of interest in neural networks?
- What are some popular libraries for implementing neural networks (e.g., TensorFlow, PyTorch, scikit-learn)?
- How can advancements in neuroscience continue to inspire the development of new artificial neural network architectures and learning algorithms?
- What is the role of weights in a perceptron? What is the bias in a perceptron?
- What is the future outlook for the field of perceptrons and neural networks? What are some emerging trends and technologies that are likely to impact the development of perceptron-like models?
- What is few-shot learning?
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