Question

your friend alex is training a deep neural network, alexnet, to classify images. he observes that the training loss is low, but that loss on a held-out validation dataset is high. validation loss can be improved by: (select all correct answers)

Answer 1

To improve Alex's validation loss on his AlexNet model, he should consider regularization, data augmentation, hyperparameter tuning, dropout, and early stopping to prevent overfitting and ensure better generalization.

Step-by-step explanation:

If your friend Alex is training a deep neural network, AlexNet, and observes that the training loss is low, but the validation loss is high, this typically indicates overfitting. To improve the validation loss, Alex can consider several strategies:

• Regularization: Techniques such as L1 or L2 regularization can help prevent overfitting by penalizing large weights in the neural network.
• Data augmentation: Expanding the training set by applying random, but realistic, modifications to the existing images can lead to a more robust model.
• Hyperparameter tuning: Optimizing the network's parameters, like learning rate or batch size, could lead to better generalization on unseen data.
• Dropping out nodes during training using the technique known as dropout to prevent the network from relying too heavily on any one node.
• Early stopping where the training process is halted once the validation loss starts to increase, even if the training loss continues to decrease.

Utilizing a combination of these methods can help reduce the validation loss and improve the model's ability to generalize to new, unseen data.