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Answer: Augmenting the training dataset with additional images to improve the model's ability to generalize., Implementing dropout layers or L2 regularization within the neural network to combat overfitting., Simplifying the neural network architecture to reduce overfitting while maintaining the model's ability to learn from the data.
The most effective strategies to address the model's performance issues under the given constraints are: - **A. Augmenting the training dataset**: Increasing the diversity and quantity of training data can help the model generalize better to unseen data. - **C. Implementing dropout layers or L2 regularization**: These techniques can prevent overfitting by introducing randomness or penalizing large weights, respectively. - **E. Simplifying the neural network architecture**: Reducing the model's complexity can help mitigate overfitting while still capturing the necessary patterns in the data. Other options are less suitable: - **B. Replacing with XGBoost**: While XGBoost is less complex, it may not capture the intricate patterns in image data as effectively as a neural network. - **D. Decreasing the learning rate**: This might help in some cases but does not directly address overfitting or the model's ability to generalize. - **F. Applying L2 Ridge Regression**: This is more appropriate for linear models and not directly applicable to the output layer of a neural network in this context.
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An industrial company is leveraging a custom deep neural network model developed with TensorFlow to enhance its quality control system. The model is designed to identify semi-finished products that should be discarded, using images captured from various stages of the production line. Despite the model showing convergence during training, the performance on the test set is not meeting expectations. The company is constrained by a tight budget and requires a solution that is scalable across multiple production lines. Given these constraints, which of the following strategies would most effectively address the model's performance issues? (Choose 3 options)
A
Augmenting the training dataset with additional images to improve the model's ability to generalize.
B
Replacing the deep neural network with a simpler model like XGBoost to reduce complexity and potential overfitting.
C
Implementing dropout layers or L2 regularization within the neural network to combat overfitting.
D
Decreasing the learning rate to ensure more precise weight updates during training.
E
Simplifying the neural network architecture to reduce overfitting while maintaining the model's ability to learn from the data.
F
Applying L2 Ridge Regression to the model's output layer to penalize large weights.