Deep Learning 面试题与答案
问题 1. What is the fundamental difference between supervised and unsupervised learning?
Supervised learning involves labeled data, where the algorithm learns from input-output pairs. Unsupervised learning deals with unlabeled data, and the algorithm discovers patterns and relationships without explicit guidance.
问题 2. Explain the concept of backpropagation in neural networks.
Backpropagation is a supervised learning algorithm used to train neural networks. It involves updating the weights of the network by calculating the gradient of the loss function with respect to the weights and adjusting them to minimize the error.
问题 3. What is the vanishing gradient problem, and how does it affect deep neural networks?
The vanishing gradient problem occurs when gradients become extremely small during backpropagation, leading to negligible weight updates in early layers. This hinders the training of deep networks, as early layers fail to learn meaningful representations.
问题 4. Differentiate between overfitting and underfitting in the context of machine learning models.
Overfitting occurs when a model learns the training data too well, capturing noise and producing poor generalization on new data. Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test sets.
问题 5. Explain the purpose of an activation function in a neural network.
An activation function introduces non-linearity to the neural network, allowing it to learn complex patterns. It transforms the input signal into an output signal, enabling the network to model and understand more intricate relationships in the data.
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