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5 cards from this section. Quick, good the night before a test.
What is the purpose of backpropagation?
Think about how gradients help in training deep learning models.
Define learning rate.
Consider what happens to the model if the learning rate is too high or too low.
What technique is used to update weights in a neural network?
Think about the method used for learning from errors.
Is the learning rate constant throughout the training process? (True/False)
Consider how learning rates can change for better optimization.
Explain how you would implement a custom learning rate scheduler in a deep learning framework. Provide a sample code snippet.
Consider how learning rates might change during training epochs.
Discuss how regularization techniques might affect model performance in different scenarios. Give an example.
Thinking about the trade-off between training accuracy and generalization.
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