NCA-GENM Question 173
Select 3You are assisting in the training of a multimodal generative AI model that combines image and text data. The senior team member has tasked you with optimizing the training process to improve model performance. Which of the following actions would most likely contribute to training optimization?
- A
Use mixed precision training to reduce memory consumption and speed up computations.
- B
Increase the size of the dataset by including uncurated, noisy data.
- C
Implement a learning rate scheduler to adjust the learning rate dynamically during training.
- D
Monitor validation loss and apply early stopping if performance plateaus.
- E
Reduce the batch size significantly to minimize computational overhead.
Show answer and explanation
Correct answers: A, C, D
Explanation
Optimizing model training involves techniques that improve efficiency, convergence, and generalization. Mixed precision training, learning rate scheduling, and early stopping are all proven methods to enhance training performance under the guidance of a senior team member. Including noisy data or significantly reducing batch size, however, can negatively impact the model's effectiveness.
- A. Correct.
Mixed precision training is a widely used optimization technique for deep learning models that reduces memory usage and speeds up training without sacrificing accuracy.
- B. Incorrect.
Including uncurated, noisy data can degrade model performance and lead to overfitting or poor generalization, so it is not an effective optimization strategy.
- C. Correct.
Using a learning rate scheduler can dynamically adjust the learning rate to improve convergence and prevent overshooting during training, making it a valuable optimization technique.
- D. Correct.
Monitoring validation loss and applying early stopping helps prevent overfitting and ensures efficient use of computational resources, contributing to training optimization.
- E. Incorrect.
Reducing the batch size significantly can lead to unstable gradients and longer training times, which is counterproductive for optimization.