What is the output of a perceptron?
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what is perceptron Informational
- 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 are principal component analysis (PCA)? How can PCA be used to reduce the dimensionality of the input data for a perceptron?
- What is boosting, and how can it be applied to create a strong classifier from multiple weak perceptrons?
- Explain the concept of sequence-to-sequence learning.
- How does momentum help accelerate the training process and escape local minima? What are the key components of the Adam optimization algorithm?
- What are the limitations of traditional RNNs (e.g., long-term dependency problem)? How do LSTMs (Long Short-Term Memory networks) and GRUs (Gated Recurrent Units) address the long-term dependency problem in RNNs?
- 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 the exploding gradient problem?
- What were the initial goals and expectations for the perceptron?
- What are Gaussian processes, and how do they compare to perceptron-based models for regression and classification?
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