Deep Learning 면접 질문과 답변
Ques 21. What is the role of the activation function in a neural network's hidden layers?
The activation function introduces non-linearity to the neural network, enabling it to learn complex patterns. Common activation functions include sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU). They allow the network to capture and model more intricate relationships in the data.
Ques 22. Explain the concept of a confusion matrix and its components in the context of classification problems.
A confusion matrix is a table that summarizes the performance of a classification algorithm. It includes metrics such as true positives, true negatives, false positives, and false negatives. These metrics help evaluate the model's accuracy, precision, recall, and F1 score.
Ques 23. What is the curse of dimensionality, and how does it affect machine learning algorithms?
The curse of dimensionality refers to the challenges and increased complexity that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the amount of data required to cover the space adequately grows exponentially. This can lead to issues such as sparsity and increased computational requirements.
Ques 24. Explain the concept of fine-tuning in transfer learning and when it is commonly applied.
Fine-tuning in transfer learning involves taking a pre-trained model and further training it on a specific task or dataset. It is commonly applied when the target task is closely related to the source task, and the pre-trained model has already learned useful features. Fine-tuning can improve performance on the target task with less training data.
Ques 25. What is the difference between online learning and batch learning in machine learning?
In online learning, the model is updated incrementally as new data becomes available, adapting to changes over time. In batch learning, the model is trained on the entire dataset in one go. Online learning is suitable for scenarios with evolving data, while batch learning is more common in offline or batch processing scenarios.
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