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Deep Learning Questions et reponses d'entretien

Question 6. What is transfer learning, and how is it used in deep learning?

Transfer learning involves using a pre-trained model on one task as the starting point for a different but related task. It leverages the knowledge gained from the source task to improve the learning of the target task, especially when data for the target task is limited.

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Question 7. Explain the concept of dropout in neural networks and its purpose.

Dropout is a regularization technique where randomly selected neurons are ignored during training. It helps prevent overfitting by ensuring that no single neuron becomes overly dependent on specific features, promoting a more robust network.

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Question 8. What is a convolutional neural network (CNN), and how is it different from a fully connected neural network?

A CNN is a type of neural network designed for processing grid-like data, such as images. It uses convolutional layers to automatically and adaptively learn hierarchical features. Unlike fully connected networks, CNNs preserve spatial relationships within the input data.

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Question 9. What is the role of the learning rate in training a neural network?

The learning rate determines the size of the steps taken during optimization. A higher learning rate may speed up convergence, but it risks overshooting the minimum. A lower learning rate ensures stability but may slow down convergence. It is a crucial hyperparameter in training neural networks.

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Question 10. Explain the concept of batch normalization and its advantages in training deep neural networks.

Batch normalization normalizes the inputs of a layer within a mini-batch, reducing internal covariate shift. It stabilizes and accelerates the training process, enables the use of higher learning rates, and acts as a form of regularization, reducing the reliance on techniques like dropout.

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