Artificial Intelligence (AI) 면접 질문과 답변
Ques 31. What are recurrent neural networks (RNNs), and how do they handle sequential data?
RNNs are neural networks designed for processing sequential data by maintaining a hidden state that captures information about previous inputs. They have loops to allow information persistence through time steps.
Example:
Predicting the next word in a sentence based on the context of previous words using an RNN.
Ques 32. How does unsupervised learning differ from semi-supervised learning?
Unsupervised learning involves training models on unlabeled data, while semi-supervised learning uses a combination of labeled and unlabeled data for training.
Example:
Training a speech recognition system with a mix of labeled audio samples (with transcriptions) and unlabeled samples.
Ques 33. What is the role of a kernel in image processing, specifically in the context of convolutional neural networks (CNNs)?
In image processing and CNNs, a kernel (filter) is a small matrix applied to input data to perform operations such as convolution, enabling the extraction of features like edges and textures.
Example:
Detecting horizontal or vertical edges in an image using convolutional kernels.
Ques 34. Explain the concept of hyperparameter tuning.
Hyperparameter tuning involves optimizing the hyperparameters of a machine learning model to achieve better performance. This is often done through techniques like grid search or random search.
Example:
Adjusting the learning rate, batch size, and the number of layers in a neural network to find the optimal combination for a given task.
Ques 35. What is reinforcement learning's exploration-exploitation tradeoff?
The exploration-exploitation tradeoff in reinforcement learning involves balancing the exploration of new actions to discover their outcomes versus exploiting known actions to maximize immediate rewards.
Example:
In a game, an agent must decide whether to try a new strategy (exploration) or stick to a known strategy (exploitation) based on past experiences.
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