Artificial Intelligence (AI) Questions et reponses d'entretien
Question 11. How does a convolutional neural network (CNN) work?
A CNN is a deep learning model designed for processing structured grid data, such as images. It uses convolutional layers to automatically and adaptively learn hierarchical features from the input data.
Example:
Image recognition tasks, where a CNN can identify objects or patterns within images.
Question 12. What is the role of activation functions in neural networks?
Activation functions introduce non-linearity to the neural network, enabling it to learn complex patterns. They decide whether a neuron should be activated or not based on the weighted sum of inputs.
Example:
The sigmoid or ReLU activation functions applied to hidden layers in a neural network.
Question 13. What are hyperparameters in machine learning models?
Hyperparameters are configuration settings for machine learning models that are not learned from data but set prior to training. Examples include learning rates, batch sizes, and the number of hidden layers in a neural network.
Example:
Adjusting the learning rate during model training to find the optimal value.
Question 14. What is the curse of dimensionality in machine learning?
The curse of dimensionality refers to the increased complexity and sparsity of data as the number of features or dimensions grows. It can lead to difficulties in model training and generalization.
Example:
In high-dimensional data, the distance between points becomes less meaningful, impacting the performance of algorithms like k-nearest neighbors.
Question 15. What is a decision tree in machine learning?
A decision tree is a flowchart-like model where each internal node represents a decision based on the value of a particular feature, and each leaf node represents the outcome of the decision process.
Example:
Predicting whether a customer will purchase a product based on their age, income, and buying history using a decision tree.
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